diff --git "a/Exponential_Smoothing_with_Holt_Winters.ipynb" "b/Exponential_Smoothing_with_Holt_Winters.ipynb"
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+      "source": [
+        "# <font color=\"#006d77\">**Forecasting with Exponential Smoothing: From Simple to Holt-Winters Method**</font>\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Overview</h3></font>\n",
+        "\n",
+        "This notebook covers different types of **Exponential Smoothing** techniques for time series forecasting:\n",
+        "\n",
+        "1. **Simple Exponential Smoothing (SES)**  \n",
+        "2. **Double Exponential Smoothing (Holt’s Method)**  \n",
+        "3. **Triple Exponential Smoothing (Holt-Winters Method)**\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Objectives</h3></font>\n",
+        "\n",
+        "- Understand the core idea of exponential smoothing  \n",
+        "- Learn how each method builds on the previous:\n",
+        "  - SES handles **level**\n",
+        "  - Holt’s method handles **level + trend**\n",
+        "  - Holt-Winters method handles **level + trend + seasonality**\n",
+        "- Apply Holt-Winters method to forecast daily revenue  \n",
+        "- Visualize and interpret the forecast results\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Dataset</h3></font>\n",
+        "\n",
+        "We will use a **daily revenue time series**, where each row corresponds to a revenue value recorded on a specific date. This data will help us:\n",
+        "\n",
+        "- Visualize time-based patterns  \n",
+        "- Train smoothing models  \n",
+        "- Forecast future values  \n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#004d4d\"><h2>Introduction to Exponential Smoothing</h2></font>\n",
+        "\n",
+        "Exponential Smoothing is a classic and widely used **time series forecasting** technique that originated in the **1950s**. Robert G. Brown introduced it in **1956**, and it was further developed by Charles C. Holt (1957) and Peter Winters (1960).\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Why the \"Exponential\" in Exponential Smoothing?</h3></font>\n",
+        "\n",
+        "The method assigns weights to past observations that **decrease exponentially** as the observations get older:\n",
+        "\n",
+        "- Most recent data points have the **highest weight**\n",
+        "- Older data points’ influence **decays geometrically** with time\n",
+        "- This gives the method a “memory” that prioritizes **recent changes**, while still considering historical patterns\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Position Between Naïve and Mean Forecasting</h3></font>\n",
+        "\n",
+        "- **Naïve forecast**: Uses only the most recent observation to project the next value  \n",
+        "- **Mean forecast**: Uses the average of all past observations  \n",
+        "- **Exponential Smoothing**: Strikes a balance between these two by:\n",
+        "  - Using **all past data**\n",
+        "  - Giving **more weight to recent values**\n",
+        "  - Allowing forecasts to be **more responsive** to recent changes\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Key Strengths</h3></font>\n",
+        "\n",
+        "- **Computationally lightweight**: Requires only the previous forecast and the latest data point  \n",
+        "- **Highly adaptable**, with extensions to handle:\n",
+        "  - **Level only** → Simple Exponential Smoothing (SES)\n",
+        "  - **Level + Trend** → Holt’s Linear Trend Method\n",
+        "  - **Level + Trend + Seasonality** → Holt–Winters Method (Triple Exponential Smoothing)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "<font color=\"#ff6600\"><h3>Sources & References</h3></font>\n",
+        "\n",
+        "- [InfluxData Beginner’s Guide](https://www.influxdata.com/blog/exponential-smoothing-beginners-guide)  \n",
+        "- [Valeman Medium Article on Brown’s Origins](https://valeman.medium.com/robert-g-browns-exponential-smoothing-origins-development-and-legacy-e23dd8cca159)p"
+      ],
+      "metadata": {
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+      }
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+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
+        "from statsmodels.tsa.holtwinters import SimpleExpSmoothing, ExponentialSmoothing\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error"
+      ],
+      "metadata": {
+        "id": "gHgFNVA9456y"
+      },
+      "execution_count": null,
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+      "source": [
+        "\n",
+        "daily_revenue_df = pd.read_csv(\"/content/daily_revenue.csv\", index_col = \"date\", parse_dates = True)\n",
+        "daily_revenue_df.head()"
+      ],
+      "metadata": {
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+        },
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+      "execution_count": null,
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+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            revenue discount_rate coupon_rate\n",
+              "date                                         \n",
+              "1/1/2018  6,270,839        34.27%       1.09%\n",
+              "2/1/2018  8,922,076        30.87%       1.08%\n",
+              "3/1/2018  8,446,101        28.11%       1.01%\n",
+              "4/1/2018  7,785,798        27.32%       0.96%\n",
+              "5/1/2018  6,375,303        25.70%       0.90%"
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+              "      <th>1/1/2018</th>\n",
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+              "      <td>34.27%</td>\n",
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+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "daily_revenue_df",
+              "summary": "{\n  \"name\": \"daily_revenue_df\",\n  \"rows\": 1795,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1795,\n        \"samples\": [\n          \"19/5/2021\",\n          \"9/8/2018\",\n          \"25/2/2022\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1795,\n        \"samples\": [\n          \"17,174,600\",\n          \"5,785,771\",\n          \"10,627,292\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount_rate\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1144,\n        \"samples\": [\n          \"13.98%\",\n          \"25.00%\",\n          \"13.96%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coupon_rate\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 287,\n        \"samples\": [\n          \"0.91%\",\n          \"1.64%\",\n          \"3.14%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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+        "# Display information about the DataFrame, including data types and non-null counts\n",
+        "daily_revenue_df.info()"
+      ],
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+          "base_uri": "https://localhost:8080/"
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+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Index: 1795 entries, 1/1/2018 to 30/11/2022\n",
+            "Data columns (total 3 columns):\n",
+            " #   Column         Non-Null Count  Dtype \n",
+            "---  ------         --------------  ----- \n",
+            " 0   revenue        1795 non-null   object\n",
+            " 1   discount_rate  1795 non-null   object\n",
+            " 2   coupon_rate    1795 non-null   object\n",
+            "dtypes: object(3)\n",
+            "memory usage: 56.1+ KB\n"
+          ]
+        }
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+      "source": [
+        "daily_revenue_df.isnull().sum()"
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+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "Ws2yfM7wNfPc",
+        "outputId": "165c8292-c246-4ce1-962e-3a21a10d8db4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "revenue          0\n",
+              "discount_rate    0\n",
+              "coupon_rate      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>revenue</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>discount_rate</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>coupon_rate</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(daily_revenue_df['revenue'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z2AyxGHvNh2R",
+        "outputId": "84f4e694-a4e8-4230-8b6c-0e555c95435c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1795"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Data Pre-processing** </font>"
+      ],
+      "metadata": {
+        "id": "vu02sBbl5qab"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Remove comma from column 'complaints' and transform the values into float\n",
+        "daily_revenue_df['revenue'] = daily_revenue_df['revenue'].str.replace(',', '').astype(int)\n",
+        "daily_revenue_df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "fXOBdKi95uvl",
+        "outputId": "9d3b86f6-1a88-4fef-c3c3-57bd1ae25171"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          revenue discount_rate coupon_rate\n",
+              "date                                       \n",
+              "1/1/2018  6270839        34.27%       1.09%\n",
+              "2/1/2018  8922076        30.87%       1.08%\n",
+              "3/1/2018  8446101        28.11%       1.01%\n",
+              "4/1/2018  7785798        27.32%       0.96%\n",
+              "5/1/2018  6375303        25.70%       0.90%"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e50e4845-1792-4514-85c0-47b39517b73f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>revenue</th>\n",
+              "      <th>discount_rate</th>\n",
+              "      <th>coupon_rate</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1/1/2018</th>\n",
+              "      <td>6270839</td>\n",
+              "      <td>34.27%</td>\n",
+              "      <td>1.09%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2/1/2018</th>\n",
+              "      <td>8922076</td>\n",
+              "      <td>30.87%</td>\n",
+              "      <td>1.08%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3/1/2018</th>\n",
+              "      <td>8446101</td>\n",
+              "      <td>28.11%</td>\n",
+              "      <td>1.01%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4/1/2018</th>\n",
+              "      <td>7785798</td>\n",
+              "      <td>27.32%</td>\n",
+              "      <td>0.96%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5/1/2018</th>\n",
+              "      <td>6375303</td>\n",
+              "      <td>25.70%</td>\n",
+              "      <td>0.90%</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e50e4845-1792-4514-85c0-47b39517b73f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e50e4845-1792-4514-85c0-47b39517b73f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e50e4845-1792-4514-85c0-47b39517b73f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d1eca287-74be-41d4-90ff-75d1b57cf8a1\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1eca287-74be-41d4-90ff-75d1b57cf8a1')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d1eca287-74be-41d4-90ff-75d1b57cf8a1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "daily_revenue_df",
+              "summary": "{\n  \"name\": \"daily_revenue_df\",\n  \"rows\": 1795,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1795,\n        \"samples\": [\n          \"19/5/2021\",\n          \"9/8/2018\",\n          \"25/2/2022\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5139488,\n        \"min\": 3820099,\n        \"max\": 77562782,\n        \"num_unique_values\": 1795,\n        \"samples\": [\n          17174600,\n          5785771,\n          10627292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount_rate\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1144,\n        \"samples\": [\n          \"13.98%\",\n          \"25.00%\",\n          \"13.96%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coupon_rate\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 287,\n        \"samples\": [\n          \"0.91%\",\n          \"1.64%\",\n          \"3.14%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "daily_revenue_df.index = pd.to_datetime(daily_revenue_df.index,dayfirst=True)"
+      ],
+      "metadata": {
+        "id": "cfh2nf1DCqcW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Exploratory Data Analysis** </font>"
+      ],
+      "metadata": {
+        "id": "JKWng1hbAa05"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the weekly complaint values\n",
+        "daily_revenue_df['revenue'].plot(title = 'daily revenue')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "CXcmxcaNIqf3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "b1ff72de-4732-42cb-815d-9cb968645633"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### <font color=\"#ff6600\">Observation</font>:\n",
+        "- From the above graph a clear yearly seasonal pattern and an increading upward trend is evident"
+      ],
+      "metadata": {
+        "id": "mT6pECGcO13P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Seasonal Decomposition** </font>\n",
+        "\n",
+        "This reveals the structure of the series by separating it into three main components:\n",
+        "\n",
+        "### Components of Time Series\n",
+        "\n",
+        "- **Trend**  \n",
+        "  The long-term progression of the series — it shows the overall direction (increasing, decreasing, or stable) over time.\n",
+        "\n",
+        "- **Seasonal**  \n",
+        "  The repeating short-term cycle or pattern at fixed intervals, such as daily, weekly, monthly, or quarterly.\n",
+        "\n",
+        "- **Residual (Irregular or Noise)**  \n",
+        "  The random variation or \"leftover\" part of the data after removing both trend and seasonal components.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Things to Consider Before Performing Seasonal Decomposition\n",
+        "\n",
+        "##### 1. Model Selection\n",
+        "\n",
+        "Before decomposing the series, it's important to choose the correct model type:\n",
+        "\n",
+        "- **Additive Model**: Use when the seasonal fluctuations are roughly constant over time.\n",
+        "- **Multiplicative Model**: Use when the seasonal variation increases or decreases with the level of the trend.\n",
+        "\n",
+        "##### 2. Identify the Seasonality Period\n",
+        "\n",
+        "You need to determine the correct **seasonal period** (e.g., 12 for monthly seasonality, 365 for yearly seasonality in daily data). This can be guided by:\n",
+        "\n",
+        "- Domain knowledge  \n",
+        "- Visual inspection of time series plots  \n",
+        "- Autocorrelation and partial autocorrelation plots  \n",
+        "- Spectral analysis\n",
+        "\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "Id8RMlK6TVCC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plotting the autocorrelation to determine seasonal period.\n",
+        "fig, ax = plt.subplots(figsize = (10,4))\n",
+        "plot_acf(daily_revenue_df['revenue'], lags = 730, ax = ax)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "daANxB6I3sSf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 372
+        },
+        "outputId": "0008b837-ff68-4a71-ce61-1322d0f69fd3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### <font color=\"#ff6600\">Observation</font> :\n",
+        "  Repeaitng spikes after every 365 interval indicate repeating seasonal patterns."
+      ],
+      "metadata": {
+        "id": "3LPdPT9Na3Sm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# In our case, we use: #model=multiplicative and period= 365\n",
+        "# This is because our daily revenue dataset exhibits **yearly seasonality** which is increasing with the trend, as observed in the earlier visualization and also in the ACF plot\n",
+        "result = seasonal_decompose(daily_revenue_df['revenue'],model = 'mul',period = 365)\n",
+        "\n",
+        "result.plot()\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "L0tqOeNZ0Z4u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "outputId": "cebaf44e-ccc6-49e2-8c8b-37832f3b8e9e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Data Preparation for Modeling** </font>\n",
+        "\n",
+        "\n",
+        "###Performing Train Test Split\n",
+        "\n",
+        "\n",
+        "- Train: all data except last 365 samples\n",
+        "- Test: contains last 365 samples\n",
+        "\n",
+        "\n",
+        "-  We will train the data on all the samples except for last 365 days and after modeling we will do predictions on the test data"
+      ],
+      "metadata": {
+        "id": "RZyLMAU8_fxf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(daily_revenue_df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "63aaG7WdF5SU",
+        "outputId": "83a0f4c4-3a9a-48bf-cb2e-458a82d95a6c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1795"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "n_samples = 365\n",
+        "train, test = daily_revenue_df.iloc[:-n_samples,0], daily_revenue_df.iloc[-n_samples:, 0 ]\n",
+        "test.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "3nxTPHd7_h8I",
+        "outputId": "9f804d1d-3a11-4fe5-9537-a71f8133db63"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "date\n",
+              "2021-12-01    13526425\n",
+              "2021-12-02    16592762\n",
+              "2021-12-03    14928804\n",
+              "2021-12-04    15696804\n",
+              "2021-12-05    18117228\n",
+              "Name: revenue, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>revenue</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2021-12-01</th>\n",
+              "      <td>13526425</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-02</th>\n",
+              "      <td>16592762</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-03</th>\n",
+              "      <td>14928804</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-04</th>\n",
+              "      <td>15696804</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-05</th>\n",
+              "      <td>18117228</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Lets Begin with Modeling by Simple Exponential Smoothing** </font>\n",
+        "\n",
+        "Simple Exponential Smoothing (SES) is a basic yet powerful method for forecasting time series data that shows **no clear trend or seasonality**. It works by assigning exponentially decreasing weights to past observations, emphasizing more recent data.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "This method uses a single parameter, **alpha (α)**, known as the **smoothing factor**. It controls how much weight is placed on the most recent observation versus the historical average.\n",
+        "\n",
+        "- If **α** is close to 1, the model gives more importance to recent data (fast adaptation).\n",
+        "- If **α** is close to 0, the model smooths more slowly, giving more weight to past observations.\n",
+        "\n",
+        "The smoothing factor **α** typically ranges between **0 and 1**.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "## Mathematical Formula\n",
+        "\n",
+        "The update rule for Simple Exponential Smoothing is given by:\n",
+        "\n",
+        "\\[\n",
+        "s_t = \\alpha x_t + (1 - \\alpha) s_{t-1}\n",
+        "\\]\n",
+        "\n",
+        "Or equivalently:\n",
+        "\n",
+        "\\[\n",
+        "s_t = s_{t-1} + \\alpha(x_t - s_{t-1})\n",
+        "\\]\n",
+        "\n",
+        "Where:\n",
+        "\n",
+        "- \\( s_t \\) = Smoothed statistic at time \\( t \\)  \n",
+        "- \\( x_t \\) = Actual value at time \\( t \\)  \n",
+        "- \\( s_{t-1} \\) = Smoothed statistic at time \\( t-1 \\)  \n",
+        "- \\( \\alpha \\) = Smoothing factor ( \\( 0 < \\alpha < 1 \\) )\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Intuition\n",
+        "\n",
+        "Think of SES as a weighted moving average where **weights decay exponentially** into the past. It’s a middle ground between:\n",
+        "\n",
+        "- **Naive forecasting**: using only the most recent value.\n",
+        "- **Simple averaging**: using the mean of all past values equally.\n",
+        "\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "WdT3l0uWEaR4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "SES_model = SimpleExpSmoothing(train).fit()\n",
+        "\n",
+        "ses_model_predictions = SES_model.forecast(len(test))"
+      ],
+      "metadata": {
+        "id": "Yn3XyldX__-M",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "29b81180-061d-4500-d0b1-a84c445c9b5e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
+            "  self._init_dates(dates, freq)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "plt.figure(figsize = (10,4))\n",
+        "\n",
+        "plt.plot(train, label = 'Train')\n",
+        "plt.plot(test, label = 'Test')\n",
+        "plt.plot(ses_model_predictions, label = \"Forecast\")\n",
+        "plt.title(\"Simple Exponential smoothing\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 387
+        },
+        "id": "csW61d4ZFL9Z",
+        "outputId": "bd70b7bd-499b-47da-b70a-098af6de1f57"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "# <font color=\"#006d77\">**Next we are implementing Double Exponential Smoothing (Holt's Linear Smoothing)** </font>\n",
+        "\n",
+        "Double Exponential Smoothing, also known as **Holt’s Trend Method**, **Second-Order Smoothing**, or **Holt’s Linear Smoothing**, extends Simple Exponential Smoothing to capture data with a **trend**. It is used when the time series data exhibits a trend but **not seasonality**.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Purpose\n",
+        "\n",
+        "The core idea behind Double Exponential Smoothing is to model both the **level** and the **trend** of the series:\n",
+        "\n",
+        "- The **level** is the smoothed estimate of the series.\n",
+        "- The **trend** captures the increasing or decreasing movement over time.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Parameters\n",
+        "\n",
+        "This method introduces an additional parameter:\n",
+        "\n",
+        "- **Alpha (α)**: Smoothing factor for the level (same as in Simple Exponential Smoothing).\n",
+        "- **Beta (β)**: Smoothing factor for the trend. Controls how quickly the trend component is updated over time.\n",
+        "\n",
+        "Both α and β range between 0 and 1.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Intuition\n",
+        "\n",
+        "Simple Exponential Smoothing works well when data has no trend, but it falls short when the data shows a consistent **upward or downward movement** over time.\n",
+        "\n",
+        "Double Exponential Smoothing solves this by:\n",
+        "\n",
+        "- Keeping track of **how fast the series is changing** using a trend component.\n",
+        "- Adjusting the forecast not just based on the most recent value, but also based on how the series has been increasing or decreasing.\n",
+        "- Using two smoothing equations: one for the level and one for the trend, so it can adapt as the trend grows stronger or weaker.\n",
+        "\n",
+        "This means the model doesn't just \"follow\" the data—it learns and **projects the direction** the data is going.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## When to Use\n",
+        "\n",
+        "- Data contains a **trend**, but **no seasonality**.\n",
+        "- Useful for **short- to medium-term forecasting** where trend behavior is observed.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Mathematical Formulas\n",
+        "\n",
+        "Let:\n",
+        "\n",
+        "- \\( s_t \\): Smoothed value (level) at time \\( t \\)\n",
+        "- \\( b_t \\): Estimated trend at time \\( t \\)\n",
+        "- \\( x_t \\): Actual observation at time \\( t \\)\n",
+        "\n",
+        "### Update equations:\n",
+        "\n",
+        "\\[\n",
+        "s_t = \\alpha x_t + (1 - \\alpha)(s_{t-1} + b_{t-1})\n",
+        "\\]\n",
+        "\n",
+        "\\[\n",
+        "b_t = \\beta (s_t - s_{t-1}) + (1 - \\beta) b_{t-1}\n",
+        "\\]\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Explanation of Terms\n",
+        "\n",
+        "- \\( \\alpha \\): Controls how much weight is given to the latest observation versus the past level plus trend.\n",
+        "- \\( \\beta \\): Controls how quickly the trend component is updated.\n",
+        "- \\( s_t \\): Represents the smoothed value (forecasted level).\n",
+        "- \\( b_t \\): Represents the best estimate of the trend at time \\( t \\).\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "\n",
+        "Double Exponential Smoothing is ideal for time series data that shows a **trend but no seasonality**. It learns both the current level and the trend of the data and uses both to make accurate forecasts."
+      ],
+      "metadata": {
+        "id": "KU362TxPItBW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "DES_model = ExponentialSmoothing(train,trend = 'mul',seasonal = None).fit()\n",
+        "des_model_predictions = DES_model.forecast(len(test))\n",
+        "des_model_predictions\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 582
+        },
+        "id": "9aveMxj5Iv2Q",
+        "outputId": "5d50e9e0-977e-4dfc-8063-228890552d91"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/holtwinters/model.py:85: RuntimeWarning: overflow encountered in matmul\n",
+            "  return err.T @ err\n",
+            "/usr/local/lib/python3.11/dist-packages/scipy/optimize/_numdiff.py:596: RuntimeWarning: invalid value encountered in subtract\n",
+            "  df = fun(x1) - f0\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2021-12-01    2.609419e+07\n",
+              "2021-12-02    2.588051e+07\n",
+              "2021-12-03    2.566858e+07\n",
+              "2021-12-04    2.545838e+07\n",
+              "2021-12-05    2.524991e+07\n",
+              "                  ...     \n",
+              "2022-11-26    1.352041e+06\n",
+              "2022-11-27    1.340969e+06\n",
+              "2022-11-28    1.329988e+06\n",
+              "2022-11-29    1.319097e+06\n",
+              "2022-11-30    1.308296e+06\n",
+              "Freq: D, Length: 365, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2021-12-01</th>\n",
+              "      <td>2.609419e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-02</th>\n",
+              "      <td>2.588051e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-03</th>\n",
+              "      <td>2.566858e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-04</th>\n",
+              "      <td>2.545838e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-05</th>\n",
+              "      <td>2.524991e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-26</th>\n",
+              "      <td>1.352041e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-27</th>\n",
+              "      <td>1.340969e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-28</th>\n",
+              "      <td>1.329988e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-29</th>\n",
+              "      <td>1.319097e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-30</th>\n",
+              "      <td>1.308296e+06</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>365 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "plt.figure(figsize = (10,4))\n",
+        "plt.plot(train, label = 'Train')\n",
+        "plt.plot(test, label = 'Test')\n",
+        "plt.plot(des_model_predictions, label = \"Forecast\")\n",
+        "plt.title(\"Double Exponential Smoothing\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 387
+        },
+        "id": "5tRQuJxTJfmw",
+        "outputId": "32505de1-bb66-4b60-e6b8-f8e0bbd26602"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Holt-Winters’ exponential smoothing(Triple Exponential Smoothing)** </font>\n",
+        "\n",
+        "This is an extension of exponential smoothing that captures **both trend and seasonality** in time series data.\n",
+        "\n",
+        "This method is ideal when your data exhibits:\n",
+        "\n",
+        "- A **linear trend**, and  \n",
+        "- A **seasonal pattern** (e.g., yearly, monthly, weekly fluctuations).\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "Triple exponential smoothing applies exponential smoothing **three times**:\n",
+        "\n",
+        "1. **Level smoothing** – to capture the baseline value\n",
+        "2. **Trend smoothing** – to account for upward/downward movement\n",
+        "3. **Seasonal smoothing** – to model repeating cycles\n",
+        "\n",
+        "It introduces a **new parameter**, gamma (γ), which controls the impact of the seasonal component.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Types of Holt-Winters Models\n",
+        "\n",
+        "There are two types, depending on the nature of the seasonality:\n",
+        "\n",
+        "- **Additive Seasonality** – Use when seasonal variations are roughly constant over time.\n",
+        "- **Multiplicative Seasonality** – Use when seasonal variations increase or decrease proportionally with the level.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Parameters\n",
+        "\n",
+        "Triple exponential smoothing uses **three parameters**:\n",
+        "\n",
+        "- \\( \\alpha \\): Level smoothing factor (intercept)\n",
+        "- \\( \\beta \\): Trend smoothing factor\n",
+        "- \\( \\gamma \\): Seasonal smoothing factor\n",
+        "\n",
+        "All three parameters lie between **0 and 1**.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Mathematical Formulation\n",
+        "\n",
+        "Let:\n",
+        "\n",
+        "- \\( s_t \\): Smoothed level at time \\( t \\)\n",
+        "- \\( b_t \\): Trend component at time \\( t \\)\n",
+        "- \\( c_t \\): Seasonal component at time \\( t \\)\n",
+        "- \\( x_t \\): Actual observation at time \\( t \\)\n",
+        "- \\( L \\): Season length (e.g., 12 for monthly seasonality over a year)\n",
+        "\n",
+        "### Initial condition:\n",
+        "\n",
+        "\\[\n",
+        "s_0 = x_0\n",
+        "\\]\n",
+        "\n",
+        "### Update equations (Multiplicative version shown):\n",
+        "\n",
+        "\\[\n",
+        "s_t = \\alpha \\left( \\frac{x_t}{c_{t-L}} \\right) + (1 - \\alpha)(s_{t-1} + b_{t-1})\n",
+        "\\]\n",
+        "\n",
+        "\\[\n",
+        "b_t = \\beta (s_t - s_{t-1}) + (1 - \\beta)b_{t-1}\n",
+        "\\]\n",
+        "\n",
+        "\\[\n",
+        "c_t = \\gamma \\left( \\frac{x_t}{s_t} \\right) + (1 - \\gamma)c_{t-L}\n",
+        "\\]\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Explanation of Terms\n",
+        "\n",
+        "- \\( s_t \\): Smoothed level (baseline estimate)\n",
+        "- \\( b_t \\): Best estimate of the trend\n",
+        "- \\( c_t \\): Seasonal component\n",
+        "- \\( \\alpha \\): Controls weight on the new level observation\n",
+        "- \\( \\beta \\): Controls how quickly the trend updates\n",
+        "- \\( \\gamma \\): Controls how quickly the seasonality adjusts\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "\n",
+        "- **Triple Exponential Smoothing** is the most sophisticated of the three smoothing techniques.\n",
+        "- It handles **level, trend, and seasonality** simultaneously.\n",
+        "- Requires more data and more computation, but provides **accurate forecasts** for time series with complex patterns.\n",
+        "\n",
+        "> Next, we’ll implement the Holt-Winters  and apply it to our revenue dataset to perform time series forecasting."
+      ],
+      "metadata": {
+        "id": "wk8AwIL9eZHs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "TES_model = ExponentialSmoothing(train,trend = 'mul',seasonal = \"mul\",seasonal_periods = 365).fit()\n",
+        "\n",
+        "tes_model_predictions = TES_model.forecast(len(test))\n",
+        "tes_model_predictions\n"
+      ],
+      "metadata": {
+        "id": "H-dkM-Oieol0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 547
+        },
+        "outputId": "d38b8232-bf14-44f3-863c-5153ca8c734e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/holtwinters/model.py:903: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2021-12-01    1.665330e+07\n",
+              "2021-12-02    1.380312e+07\n",
+              "2021-12-03    1.334726e+07\n",
+              "2021-12-04    1.339762e+07\n",
+              "2021-12-05    1.446078e+07\n",
+              "                  ...     \n",
+              "2022-11-26    5.544928e+07\n",
+              "2022-11-27    4.044769e+07\n",
+              "2022-11-28    3.175093e+07\n",
+              "2022-11-29    2.486729e+07\n",
+              "2022-11-30    1.944151e+07\n",
+              "Freq: D, Length: 365, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2021-12-01</th>\n",
+              "      <td>1.665330e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-02</th>\n",
+              "      <td>1.380312e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-03</th>\n",
+              "      <td>1.334726e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-04</th>\n",
+              "      <td>1.339762e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2021-12-05</th>\n",
+              "      <td>1.446078e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-26</th>\n",
+              "      <td>5.544928e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-27</th>\n",
+              "      <td>4.044769e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-28</th>\n",
+              "      <td>3.175093e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-29</th>\n",
+              "      <td>2.486729e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-11-30</th>\n",
+              "      <td>1.944151e+07</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>365 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the size of the plot to 10 inches by 4 inches\n",
+        "plt.figure(figsize = (10,4))\n",
+        "\n",
+        "# Plot train, test, and forecasts values\n",
+        "plt.plot(train, label = 'Train')\n",
+        "plt.plot(test, label = 'Test')\n",
+        "plt.plot(tes_model_predictions, label = \"Forecast\")\n",
+        "\n",
+        "# add title and legend to the plot\n",
+        "plt.title(\"Train, Test and Predictions with Triple Exponential Smoothing\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 387
+        },
+        "outputId": "4e6694d9-74f5-4975-ccc5-abd87c37e6b4",
+        "id": "VgKYInnMeol3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "rmse = mean_squared_error(test, tes_model_predictions)\n",
+        "mape = mean_absolute_percentage_error(test, tes_model_predictions)\n",
+        "\n",
+        "\n",
+        "print(f\"RMSE: {rmse:.2f}\")\n",
+        "print(f\"MAPE: {100 * mape:.2f} %\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lJV4SKNIeh4k",
+        "outputId": "254d9ea1-d835-48c0-97ef-1ecf5879416a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 13746925771359.20\n",
+            "MAPE: 13.47 %\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "plt.figure(figsize = (15,3))\n",
+        "\n",
+        "plt.plot(test, label = 'Actual')\n",
+        "plt.plot(tes_model_predictions, label = \"Forecast\")\n",
+        "plt.title(f\"Actual vs Predicted\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 216
+        },
+        "id": "yHtGikprPNJz",
+        "outputId": "c28fc164-39de-440b-9e15-5b30c80cb4aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### <font color=\"#ff6600\">Final Take</font>\n",
+        "\n",
+        "\n",
+        "- As we progress from simple to triple exponential smoothing, each method captures more complexity — starting from level-only modeling, then incorporating trend, and finally accounting for seasonality.\n",
+        "\n",
+        "- This progression leads to **increasingly accurate predictions**, especially for real-world time series where trends and seasonality play a significant role."
+      ],
+      "metadata": {
+        "id": "FNiUpu-E40oU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# <font color=\"#006d77\">**Next we fit and  train the model on the entire data and make future predictions** </font>"
+      ],
+      "metadata": {
+        "id": "sMQ7C415QQzg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "model = ExponentialSmoothing(daily_revenue_df.revenue,trend = 'mul',seasonal = \"mul\",seasonal_periods = 365).fit()\n",
+        "future_forecast = model.forecast(365)\n",
+        "future_forecast"
+      ],
+      "metadata": {
+        "id": "o1FhdOpnRDqD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 547
+        },
+        "outputId": "fca2e574-4a94-4ef8-fbc8-d16c13d8f4a2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
+            "  self._init_dates(dates, freq)\n",
+            "/usr/local/lib/python3.11/dist-packages/statsmodels/tsa/holtwinters/model.py:903: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2022-12-01    1.627377e+07\n",
+              "2022-12-02    1.518865e+07\n",
+              "2022-12-03    1.430902e+07\n",
+              "2022-12-04    1.452563e+07\n",
+              "2022-12-05    1.605225e+07\n",
+              "                  ...     \n",
+              "2023-11-26    4.725705e+07\n",
+              "2023-11-27    3.918953e+07\n",
+              "2023-11-28    3.422469e+07\n",
+              "2023-11-29    2.237229e+07\n",
+              "2023-11-30    1.911809e+07\n",
+              "Freq: D, Length: 365, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2022-12-01</th>\n",
+              "      <td>1.627377e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-12-02</th>\n",
+              "      <td>1.518865e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-12-03</th>\n",
+              "      <td>1.430902e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-12-04</th>\n",
+              "      <td>1.452563e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2022-12-05</th>\n",
+              "      <td>1.605225e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-11-26</th>\n",
+              "      <td>4.725705e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-11-27</th>\n",
+              "      <td>3.918953e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-11-28</th>\n",
+              "      <td>3.422469e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-11-29</th>\n",
+              "      <td>2.237229e+07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-11-30</th>\n",
+              "      <td>1.911809e+07</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>365 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Set the size of the plot to 10 inches by 4 inches\n",
+        "plt.figure(figsize = (10,4))\n",
+        "\n",
+        "# Plot train and forecast values\n",
+        "plt.plot(daily_revenue_df.revenue, label = 'Train')\n",
+        "plt.plot(future_forecast, label = \"Future_Forecast\")\n",
+        "\n",
+        "# Add title and legend to the plot\n",
+        "plt.title(\"Future Forecast with Triple Exponential Smoothing\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 387
+        },
+        "outputId": "01f0b509-667d-4407-e23a-2f158e1dd17f",
+        "id": "fu4KhwWIRDqG"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file