Feat/MIDAS transformer

CHANGELOG.md

@@ -21,9 +21,11 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
   - `XGBModel` now leverages XGBoost's native Quantile Regression support that was released in version 2.0.0 for improved probabilistic forecasts. [#2051](https://github.com/unit8co/darts/pull/2051) by [Dennis Bader](https://github.com/dennisbader).
 - Other improvements:
   - Added support for time index time zone conversion with parameter `tz` before generating/computing holidays and datetime attributes. Support was added to all Time Axis Encoders, standalone encoders and forecasting models' `add_encoders`, time series generation utils functions `holidays_timeseries()` and `datetime_attribute_timeseries()`, and `TimeSeries` methods `add_datetime_attribute()` and `add_holidays()`. [#2054](https://github.com/unit8co/darts/pull/2054) by [Dennis Bader](https://github.com/dennisbader).
+  - Added new data transformer: `MIDAS`, which uses mixed-data sampling to convert `TimeSeries` from high frequency to low frequency (and back). [#1820](https://github.com/unit8co/darts/pull/1820) by [Boyd Biersteker](https://github.com/Beerstabr), [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader).
   - Added optional keyword arguments dict `kwargs` to `ExponentialSmoothing` that will be passed to the constructor of the underlying `statsmodels.tsa.holtwinters.ExponentialSmoothing` model. [#2059](https://github.com/unit8co/darts/pull/2059) by [Antoine Madrona](https://github.com/madtoinou).
   - Added new dataset `ElectricityConsumptionZurichDataset`: The dataset contains the electricity consumption of households in Zurich, Switzerland from 2015-2022 on different grid levels. We also added weather measurements for Zurich which can be used as covariates for modelling. [#2039](https://github.com/unit8co/darts/pull/2039) by [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader).
   - Added new arguments `fit_kwargs` and `predict_kwargs` to `historical_forecasts()`, `backtest()` and `gridsearch()` that will be passed to the model's `fit()` and / or `predict` methods. E.g., you can now set a batch size, static validation series, ... depending on the model support. [#2050](https://github.com/unit8co/darts/pull/2050) by [Antoine Madrona](https://github.com/madtoinou)
+  - For transparency, we issue a (removable) warning when performing auto-regressive forecasts with past covariates (with `n >= output_chunk_length`) to inform users that future values of past covariates will be accessed. [#2049](https://github.com/unit8co/darts/pull/2049) by [Antoine Madrona](https://github.com/madtoinou)
 
 **Fixed**
 - Fixed a bug when calling optimized `historical_forecasts()` for a `RegressionModel` trained with unequal component-specific lags. [#2040](https://github.com/unit8co/darts/pull/2040) by [Antoine Madrona](https://github.com/madtoinou).

darts/dataprocessing/transformers/__init__.py

@@ -9,6 +9,7 @@
 from .fittable_data_transformer import FittableDataTransformer
 from .invertible_data_transformer import InvertibleDataTransformer
 from .mappers import InvertibleMapper, Mapper
+from .midas import MIDAS
 from .missing_values_filler import MissingValuesFiller
 from .reconciliation import (
     BottomUpReconciliator,

darts/dataprocessing/transformers/boxcox.py

@@ -67,10 +67,10 @@ def __init__(
             Specifies which method to use to find an optimal value for the lmbda parameter.
             Either 'mle' or 'pearsonr'. Ignored if `lmbda` is not `None`.
         global_fit
-            Optionally, whether all of the `TimeSeries` passed to the `fit()` method should be used to fit
+            Optionally, whether all `TimeSeries` passed to the `fit()` method should be used to fit
             a *single* set of parameters, or if a different set of parameters should be independently fitted
             to each provided `TimeSeries`. If `True`, then a `Sequence[TimeSeries]` is passed to `ts_fit`
-            and a single set of parameters is fitted using all of the provided `TimeSeries`. If `False`, then
+            and a single set of parameters is fitted using all provided `TimeSeries`. If `False`, then
             each `TimeSeries` is individually passed to `ts_fit`, and a different set of fitted parameters
             if yielded for each of these fitting operations. See `FittableDataTransformer` documentation for
             further details.
@@ -136,7 +136,7 @@ def ts_fit(
         params: Mapping[str, Any],
         *args,
         **kwargs
-    ) -> Union[Sequence[float], pd.core.series.Series]:
+    ) -> Union[Sequence[float], pd.Series]:
         lmbda, method = params["fixed"]["_lmbda"], params["fixed"]["_optim_method"]
         # If `global_fit` is `True`, then `series` will be ` Sequence[TimeSeries]`;
         # otherwise, `series` is a single `TimeSeries`:

darts/dataprocessing/transformers/fittable_data_transformer.py

@@ -64,10 +64,10 @@ def __init__(
             be passed as a keyword argument, but won't automatically be applied to the input timeseries.
             See `apply_component_mask` method of `BaseDataTransformer` for further details.
         global_fit
-            Optionally, whether all of the `TimeSeries` passed to the `fit()` method should be used to fit
+            Optionally, whether all `TimeSeries` passed to the `fit()` method should be used to fit
             a *single* set of parameters, or if a different set of parameters should be independently fitted
             to each provided `TimeSeries`. If `True`, then a `Sequence[TimeSeries]` is passed to `ts_fit`
-            and a single set of parameters is fitted using all of the provided `TimeSeries`. If `False`, then
+            and a single set of parameters is fitted using all provided `TimeSeries`. If `False`, then
             each `TimeSeries` is individually passed to `ts_fit`, and a different set of fitted parameters
             if yielded for each of these fitting operations. See `ts_fit` for further details.
 
@@ -83,9 +83,9 @@ def __init__(
         of fitted parameters.
 
         Note that if an invertible *and* fittable data transformer is to be globally fitted, the data transformer
-        class should first inherit from `FittableDataTransformer` and then from `InveritibleDataTransformer`. In
-        other words, `MyTransformer(FittableDataTransformer, InveritibleDataTransformer)` is correct, but
-        `MyTransformer(InveritibleDataTransformer, FittableDataTransformer)` is **not**. If this is not implemented
+        class should first inherit from `FittableDataTransformer` and then from `InvertibleDataTransformer`. In
+        other words, `MyTransformer(FittableDataTransformer, InvertibleDataTransformer)` is correct, but
+        `MyTransformer(InvertibleDataTransformer, FittableDataTransformer)` is **not**. If this is not implemented
         correctly, then the `global_fit` parameter will not be correctly passed to `FittableDataTransformer`'s
         constructor.