corrected ILI horizon lengths

README.md

@@ -12,7 +12,7 @@ Official PyTorch code repository for the [DeepTime paper](https://arxiv.org/abs/
   non-stationary time-series forecasting.
 * Experiments on real world datases in the long sequence time-series forecasting setting demonstrates that DeepTime
   achieves competitive results with state-of-the-art methods and is highly efficient.
-
+  
 ## Requirements
 
 Dependencies for this project can be installed by:

experiments/configs/ILI/24M.gin

@@ -33,5 +33,5 @@ ForecastDataset.cross_learn = False
 ForecastDataset.time_features = []
 ForecastDataset.normalise_time_features = True
 ForecastDataset.features = 'M'
-ForecastDataset.horizon_len = 96
+ForecastDataset.horizon_len = 24
 ForecastDataset.lookback_mult = 9

experiments/configs/ILI/36M.gin

@@ -33,5 +33,5 @@ ForecastDataset.cross_learn = False
 ForecastDataset.time_features = []
 ForecastDataset.normalise_time_features = True
 ForecastDataset.features = 'M'
-ForecastDataset.horizon_len = 192
+ForecastDataset.horizon_len = 36
 ForecastDataset.lookback_mult = 7

experiments/configs/ILI/48M.gin

@@ -33,5 +33,5 @@ ForecastDataset.cross_learn = False
 ForecastDataset.time_features = []
 ForecastDataset.normalise_time_features = True
 ForecastDataset.features = 'M'
-ForecastDataset.horizon_len = 336
+ForecastDataset.horizon_len = 48
 ForecastDataset.lookback_mult = 5

experiments/configs/ILI/60M.gin

@@ -33,5 +33,5 @@ ForecastDataset.cross_learn = False
 ForecastDataset.time_features = []
 ForecastDataset.normalise_time_features = True
 ForecastDataset.features = 'M'
-ForecastDataset.horizon_len = 720
+ForecastDataset.horizon_len = 60
 ForecastDataset.lookback_mult = 3