update hourly prediction lengths

take prediction lengths from official experiments https://github.com/rakshitha123/TSForecasting/blob/master/experiments/deep_learning_experiments.py

monash_tsf.py

@@ -121,6 +121,7 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
             description="5 time series representing the half hourly electricity demand of 5 states in Australia: Victoria, New South Wales, Queensland, Tasmania and South Australia.",
             url="https://zenodo.org/record/4659727",
             file_name="australian_electricity_demand_dataset.zip",
+            prediction_length=336,
         ),
         MonashTSFBuilderConfig(
             name="wind_farms_minutely",
@@ -142,6 +143,7 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
             description="Hourly pedestrian counts captured from 66 sensors in Melbourne city starting from May 2009.",
             url="https://zenodo.org/record/4656626",
             file_name="pedestrian_counts_dataset.zip",
+            prediction_length=24,
         ),
         MonashTSFBuilderConfig(
             name="vehicle_trips",
@@ -191,6 +193,7 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
             description="137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006.",
             url="https://zenodo.org/record/4656144",
             file_name="solar_10_minutes_dataset.zip",
+            prediction_length=1008,
         ),
         MonashTSFBuilderConfig(
             name="solar_weekly",
@@ -198,6 +201,7 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
             description="137 time series representing the weekly solar power production in Alabama state in 2006.",
             url="https://zenodo.org/record/4656151",
             file_name="solar_weekly_dataset.zip",
+            prediction_length=5,
         ),
         MonashTSFBuilderConfig(
             name="car_parts",
@@ -399,10 +403,14 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
             features = datasets.Features(
                 {
                     "start": datasets.Value("timestamp[s]"),
-                    "target": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                    "target": datasets.Sequence(
+                        datasets.Sequence(datasets.Value("float32"))
+                    ),
                     "feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
                     # "feat_static_real":  datasets.Sequence(datasets.Value("float32")),
-                    "feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                    "feat_dynamic_real": datasets.Sequence(
+                        datasets.Sequence(datasets.Value("float32"))
+                    ),
                     # "feat_dynamic_cat": datasets.Sequence(datasets.Sequence(datasets.Value("uint64"))),
                     "item_id": datasets.Value("string"),
                 }
@@ -414,7 +422,9 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
                     "target": datasets.Sequence(datasets.Value("float32")),
                     "feat_static_cat": datasets.Sequence(datasets.Value("uint64")),
                     # "feat_static_real":  datasets.Sequence(datasets.Value("float32")),
-                    "feat_dynamic_real": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                    "feat_dynamic_real": datasets.Sequence(
+                        datasets.Sequence(datasets.Value("float32"))
+                    ),
                     # "feat_dynamic_cat": datasets.Sequence(datasets.Sequence(datasets.Value("uint64"))),
                     "item_id": datasets.Value("string"),
                 }
@@ -474,7 +484,7 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
             prediction_length_map = {
                 "S": 60,
                 "T": 60,
-                "H": 48,
+                "H": 168,
                 "D": 30,
                 "W": 8,
                 "M": 12,
@@ -496,13 +506,17 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
                 start = ts.start_timestamp[0]
 
                 if self.config.target_fields is not None:
-                    target_fields = ts[ts[self.config.data_column].isin(self.config.target_fields)]
+                    target_fields = ts[
+                        ts[self.config.data_column].isin(self.config.target_fields)
+                    ]
                 else:
                     target_fields = self.config.data_column.unique()
 
                 if self.config.feat_dynamic_real_fields is not None:
                     feat_dynamic_real_fields = ts[
-                        ts[self.config.data_column].isin(self.config.feat_dynamic_real_fields)
+                        ts[self.config.data_column].isin(
+                            self.config.feat_dynamic_real_fields
+                        )
                     ]
                     feat_dynamic_real = np.vstack(feat_dynamic_real_fields.target)
                 else:
@@ -513,7 +527,10 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
                 feat_static_cat = [cat]
 
                 if split in ["train", "val"]:
-                    offset = forecast_horizon * self.config.rolling_evaluations + forecast_horizon * (split == "train")
+                    offset = (
+                        forecast_horizon * self.config.rolling_evaluations
+                        + forecast_horizon * (split == "train")
+                    )
                     target = target[..., :-offset]
                     if self.config.feat_dynamic_real_fields is not None:
                         feat_dynamic_real = feat_dynamic_real[..., :-offset]
@@ -527,18 +544,25 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
                 }
         else:
             if self.config.target_fields is not None:
-                target_fields = loaded_data[loaded_data[self.config.data_column].isin(self.config.target_fields)]
+                target_fields = loaded_data[
+                    loaded_data[self.config.data_column].isin(self.config.target_fields)
+                ]
             else:
                 target_fields = loaded_data
             if self.config.feat_dynamic_real_fields is not None:
                 feat_dynamic_real_fields = loaded_data[
-                    loaded_data[self.config.data_column].isin(self.config.feat_dynamic_real_fields)
+                    loaded_data[self.config.data_column].isin(
+                        self.config.feat_dynamic_real_fields
+                    )
                 ]
             else:
                 feat_dynamic_real_fields = None
 
             for cat, ts in target_fields.iterrows():
-                start = ts.get("start_timestamp", datetime.strptime("1900-01-01 00-00-00", "%Y-%m-%d %H-%M-%S"))
+                start = ts.get(
+                    "start_timestamp",
+                    datetime.strptime("1900-01-01 00-00-00", "%Y-%m-%d %H-%M-%S"),
+                )
                 target = ts.target
                 if feat_dynamic_real_fields is not None:
                     feat_dynamic_real = np.vstack(feat_dynamic_real_fields.target)
@@ -552,7 +576,10 @@ class MonashTSF(datasets.GeneratorBasedBuilder):
                     item_id = ts.series_name
 
                 if split in ["train", "val"]:
-                    offset = forecast_horizon * self.config.rolling_evaluations + forecast_horizon * (split == "train")
+                    offset = (
+                        forecast_horizon * self.config.rolling_evaluations
+                        + forecast_horizon * (split == "train")
+                    )
                     target = target[..., :-offset]
                     if feat_dynamic_real is not None:
                         feat_dynamic_real = feat_dynamic_real[..., :-offset]