Fix/fix various issues

darts/dataprocessing/dtw/dtw.py

@@ -2,9 +2,12 @@
 from typing import Callable, Union
 
 import numpy as np
+import pandas as pd
+import xarray as xr
 
 from darts import TimeSeries
 from darts.logging import get_logger, raise_if, raise_if_not
+from darts.timeseries import DIMS, HIERARCHY_TAG, STATIC_COV_TAG
 
 from .cost_matrix import CostMatrix
 from .window import CRWindow, NoWindow, Window
@@ -203,25 +206,40 @@ def warped(self) -> (TimeSeries, TimeSeries):
             Two new TimeSeries instances of the same length, indexed by pd.RangeIndex.
         """
 
-        series1 = self.series1
-        series2 = self.series2
-
-        xa1 = series1.data_array(copy=False)
-        xa2 = series2.data_array(copy=False)
-
+        xa1 = self.series1.data_array(copy=False)
+        xa2 = self.series2.data_array(copy=False)
         path = self.path()
 
-        warped_series1 = xa1[path[:, 0]]
-        warped_series2 = xa2[path[:, 1]]
-
-        time_dim1 = series1._time_dim
-        time_dim2 = series2._time_dim
+        values1, values2 = xa1.values[path[:, 0]], xa2.values[path[:, 1]]
+
+        # We set a RangeIndex for both series:
+        warped_series1 = xr.DataArray(
+            data=values1,
+            dims=xa1.dims,
+            coords={
+                self.series1._time_dim: pd.RangeIndex(values1.shape[0]),
+                DIMS[1]: xa1.coords[DIMS[1]],
+            },
+            attrs={
+                STATIC_COV_TAG: xa1.attrs[STATIC_COV_TAG],
+                HIERARCHY_TAG: xa1.attrs[HIERARCHY_TAG],
+            },
+        )
 
-        range_index = True
+        warped_series2 = xr.DataArray(
+            data=values2,
+            dims=xa2.dims,
+            coords={
+                self.series2._time_dim: pd.RangeIndex(values2.shape[0]),
+                DIMS[1]: xa2.coords[DIMS[1]],
+            },
+            attrs={
+                STATIC_COV_TAG: xa2.attrs[STATIC_COV_TAG],
+                HIERARCHY_TAG: xa2.attrs[HIERARCHY_TAG],
+            },
+        )
 
-        if range_index:
-            warped_series1 = warped_series1.reset_index(dims_or_levels=time_dim1)
-            warped_series2 = warped_series2.reset_index(dims_or_levels=time_dim2)
+        time_dim1, time_dim2 = self.series1._time_dim, self.series2._time_dim
 
         # todo: prevent time information being lost after warping
         # Applying time index from series1 to series2 (take_dates = True) is disabled for consistency reasons

docs/userguide/timeseries.md

@@ -33,7 +33,7 @@ In addition, some models can work on *multiple time series*, meaning that they c
 
 * **Example of a multivariate series:** The blood pressure and heart rate of a single patient over time (one multivariate series with 2 components).
 
-* **Example of multiple series:** The blood pressure and heart rate of multiple patients; potentially measured at different times for different patients (one univariate series per patient).
+* **Example of multiple series:** The blood pressure and heart rate of multiple patients; potentially measured at different times for different patients (one multivariate series with 2 components per patient).
 
 
 ### Should I use a multivariate series or multiple series for my problem?
@@ -50,9 +50,9 @@ In Darts, probabilistic forecasts are represented by drawing Monte Carlo samples
 ## Creating `TimeSeries`
 `TimeSeries` objects can be created using factory methods, for example:
 
-* [TimeSeries.from_dataframe()](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_dataframe) can create `TimeSeries` from a Pandas Dataframe having one or several columns representing values (several columns would correspond to a multivariate series).
+* [TimeSeries.from_dataframe()](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_dataframe) can create `TimeSeries` from a Pandas Dataframe having one or several columns representing values (columns correspond to components, and several columns would correspond to a multivariate series).
 
-* [TimeSeries.from_values()](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_values) can create `TimeSeries` from a 2-D or 3-D NumPy array. It will generate an integer-based time index (of type `pandas.RangeIndex`). 2-D corresponds to deterministic (potentially multivariate) series, and 3-D to stochastic series.
+* [TimeSeries.from_values()](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_values) can create `TimeSeries` from a 1-D, 2-D or 3-D NumPy array. It will generate an integer-based time index (of type `pandas.RangeIndex`). 1-D corresponds to univariate deterministic series, 2-D to multivariate deterministic series, and 3-D to multivariate stochastic series.
 
 * [TimeSeries.from_times_and_values()](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_times_and_values) is similar to `TimeSeries.from_values()` but also accepts a time index.
 
@@ -67,7 +67,7 @@ my_multivariate_series = concatenate([series1, series2, ...], axis=1)
 produces a multivariate series from some series that share the same time axis.
 
 ## Implementation
-Behind the scenes, `TimeSeries` is wrapping around a 3-dimensional `xarray.DataArray` object. The dimensions are *(time, component, sample)*, where the size of the *component* dimension is larger than 1 for multivariate series and the size of the *sample* dimension is larger than 1 for stochastic series. The `DataArray` is itself backed by a a 3-dimensional NumPy array, and it has a time index (either `pandas.DatetimeIndex` or `pandas.RangeIndex`) on the *time* dimension and another `pandas.Index` on the *component* (or "columns") dimension. `TimeSeries` is intended to be immutable.
+Behind the scenes, `TimeSeries` is wrapping around a 3-dimensional `xarray.DataArray` object. The dimensions are *(time, component, sample)*, where the size of the *component* dimension is larger than 1 for multivariate series and the size of the *sample* dimension is larger than 1 for stochastic series. The `DataArray` is itself backed by a 3-dimensional NumPy array, and it has a time index (either `pandas.DatetimeIndex` or `pandas.RangeIndex`) on the *time* dimension and another `pandas.Index` on the *component* (or "columns") dimension. `TimeSeries` is intended to be immutable and most operations return new `TimeSeries` objects.
 
 ## Exporting data from a `TimeSeries`
 `TimeSeries` objects offer a few ways to export the data, for example:

requirements/core.txt

@@ -12,7 +12,7 @@ prophet>=1.1
 requests>=2.22.0
 scikit-learn>=1.0.1
 scipy>=1.3.2
-statsforecast>=0.5.2
+statsforecast==0.6.0
 statsmodels>=0.13.0
 tbats>=1.1.0
 tqdm>=4.60.0