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license: mit |
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language: |
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- en |
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metrics: |
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- mae |
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- r_squared |
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- mape |
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- mse |
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pipeline_tag: time-series-forecasting |
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datasets: |
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- Captain-Slow/Financial_datasets |
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--- |
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Welcome to this repository of Jupyter notebooks focused on **time series analysis** and **forecasting**, with applications to **financial datasets**. The goal of this collection is to explore patterns, trends, and predictive modeling techniques using both **statistical** and **machine learning** methods. |
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## What’s Inside |
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This repository includes the following: |
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- **Exploratory Data Analysis (EDA)** |
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Techniques for visualizing, decomposing, and understanding temporal structures in financial time series. |
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- **Classical Forecasting Methods** |
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Models such as: |
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- ARIMA / SARIMA |
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- Facebook Prophet |
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- Vector Auto Regression |
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- Arch/Garch for volatility modeling |
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- **Machine Learning Approaches** |
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Implementation of: |
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- Random Forests for time series regression |
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- XGBoost for trend and anomaly prediction |
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- Long Short Term Memory |
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- **Feature Engineering for Time Series** |
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Lag features, rolling statistics, seasonal indicators, and date-based encodings. |
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- **Model Optimization and Evaluation** |
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Grid-search-cv , Randomized-searhc-cv, cross-validation for time series, and performance metrics (MAE, RMSE, MAPE). |
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## Datasets |
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The notebooks primarily work with **financial datasets**, such as: |
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- Stock price data. |
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- Commodity Prices. |
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- Foreign Exchnage rates. |
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- Inflation rates. |
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- Cryptocurrency price histories. |
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- Sales datasets |
