query: MAPE yielding value of 1000+ for timeseries forecasting
positive_passages.docid: 27832
positive_passages.text: What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?
negative_passages.docid: 2187
negative_passages.text: Combinef in R HTS package- constrain to keep forecasts positive?
negative_passages.docid: 54619
negative_passages.text: Expressing hourly temporal recurrence
negative_passages.docid: 134526
negative_passages.text: Interpretation of mean absolute scaled error (MASE)
negative_passages.docid: 142341
negative_passages.text: Environment.TickCount vs DateTime.Now
negative_passages.docid: 158190
negative_passages.text: Fitting t distribtution to financial data
negative_passages.docid: 134018
negative_passages.text: Pooled time series regression in R
negative_passages.docid: 122865
negative_passages.text: Rare events modelling - value of coefficients
negative_passages.docid: 34031
negative_passages.text: Modelling Time Series of percentages
negative_passages.docid: 94709
negative_passages.text: NO2 CSV downloaded files shows zero values for consider date range using Google Earth Engine
negative_passages.docid: 140235
negative_passages.text: Recursive time series forecasting model
negative_passages.docid: 135432
negative_passages.text: Historical data appears seasonal, but forecasts from auto.arima are linear
negative_passages.docid: 84530
negative_passages.text: Size of Mean Squared Error (MSE)
negative_passages.docid: 32193
negative_passages.text: Create heatmap using pandas TimeSeries
negative_passages.docid: 90969
negative_passages.text: De normalize predicted value
negative_passages.docid: 87610
negative_passages.text: Is there any standard / criteria of good forecast measured by SMAPE and MASE?
negative_passages.docid: 123355
negative_passages.text: Compute the expected number of records by time n