Time Series Forecasting
Safetensors
granite_tsfm
tinytimemixer
ttm4hvac
tsfm
digital twin
hvac
energy
experiment
ttm4hvac-target-default / preprocessor_config.json
Ferran Aran Domingo
feat(tsp): add tsp checkpoint
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{
"categorical_columns": [],
"categorical_encoder": null,
"conditional_columns": [],
"context_length": 512,
"control_columns": [
"Heating Setpoint (C)",
"Cooling Setpoint (C)"
],
"encode_categorical": false,
"feature_extractor_type": "TimeSeriesPreprocessor",
"freq": "15min",
"frequency_mapping": {
"10min": 4,
"15min": 5,
"2min": 2,
"30min": 6,
"5min": 3,
"D": 8,
"H": 7,
"W": 9,
"d": 8,
"h": 7,
"min": 1,
"oov": 0
},
"id_columns": [],
"observable_columns": [
"Outdoor Air Temperature (C)",
"Outdoor Humidity (%)",
"Wind Speed (m/s)",
"Direct Solar Radiation (W/m^2)"
],
"prediction_length": 96,
"processor_class": "TimeSeriesPreprocessor",
"scale_categorical_columns": true,
"scaler_dict": {
"0": {
"copy": true,
"feature_names_in_": [
"Outdoor Air Temperature (C)",
"Outdoor Humidity (%)",
"Wind Speed (m/s)",
"Direct Solar Radiation (W/m^2)",
"Heating Setpoint (C)",
"Cooling Setpoint (C)"
],
"mean_": [
11.390493131575868,
0.4956289851215924,
4.1970175874735585,
278.3432437873138,
17.499021526418787,
27.500978473581213
],
"n_features_in_": 6,
"n_samples_seen_": 24528,
"scale_": [
11.317891632086864,
0.22545425324222762,
1.9710404456834305,
363.18039056252474,
2.9578743328627466,
2.9578743328627466
],
"var_": [
128.09467099566186,
0.0508296203050105,
3.885000438519936,
131899.996089148,
8.749020569008238,
8.749020569008238
],
"with_mean": true,
"with_std": true
}
},
"scaler_type": "standard",
"scaling": true,
"scaling_id_columns": [],
"scaling_id_columns_types": [],
"static_categorical_columns": [],
"target_columns": [
"Room Air Temperature (C)",
"HVAC Power Consumption (W)"
],
"target_scaler_dict": {
"0": {
"copy": true,
"feature_names_in_": [
"Room Air Temperature (C)",
"HVAC Power Consumption (W)"
],
"mean_": [
22.793821579667963,
297.210481104862
],
"n_features_in_": 2,
"n_samples_seen_": 24528,
"scale_": [
3.289274282514853,
519.4272208283594
],
"var_": [
10.819325305613603,
269804.6377374732
],
"with_mean": true,
"with_std": true
}
},
"time_series_task": "forecasting",
"timestamp_column": "time"
}