Comments (2)
capelastegui
commented on August 29, 2024
Problem is related to:
- model_naive()
- where df_actuals has samples with weight 0, at the beginning.
I think model_utils.get_normalized_x_from_date() has problems with null values
In run_forecast_single(), we pass df_actuals_interpolated rather than df_actuals to fit_model() and extrapolate_model(). We may be missing interactions between the interpolation logic and 0-weight samples in our unit tests
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capelastegui
commented on August 29, 2024
Fixed on the dev branch, need to prepare a bugfix release
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Related Issues (20)
- Update forecast loop
- In forecast_plot, identify input samples with weight=0
- metadata - In fit_time column, use integer value with seconds
- Error in date format of plot tooltip
- Smart detection of default naive model
- DeprecationWarning: assertEquals
- In model functions, cache costly, redundant operations
- model_utils.interpolate_df() - Make x column independent of sampling frequency.
- Naive model - Length of passed values is 100, index implies 88. HOT 1
- Error in detect_freq: TypeError: '>=' not supported between instances of 'numpy.float64' and 'Timedelta'
- Error: Weekly Naive model used on series < 7 samples HOT 1
- Typo in the variable name for 'calander_ita' forecast model
- Improved model_spike
- Documentation: Add install guide for M1 Macs
- Documentation: Explain use of naive models
- Not much success forecasting a Stair-Step pattern HOT 2
- Forecast works on 0.1.8 level but breaks at the 0.2.1 level HOT 2
- Veracode Scan Trigger - December 2023
- interpolate_df() changes datetime unit of date column
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