Add DeadlineMovingAverageModel (#827)

* DeadlineMovingAverageModel

* some fixes for deadline model

* add CHANGELOG.md

* add test for pipeline.backtest

* fix changelog conflict

* fix lint mistake

* fix behavior of deadline model in big horizons

* fix docs and lint mistakes

* fix docstrings and add test for big horizon

* fix linter mistake

* add test for big horizon

* fix lint mistake

* update CHANGELOG.md

Co-authored-by: ext.ytarasyuk <ext.ytarasyuk@nbubnt410.tcsbank.ru>

CHANGELOG.md

@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - 
 - 
-- 
+- `DeadlineMovingAverageModel` ([#827](https://github.com/tinkoff-ai/etna/pull/827))
 - `DirectEnsemble` ([#824](https://github.com/tinkoff-ai/etna/pull/824))
 - 
 - 
@@ -494,4 +494,4 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - Distribution plot
   - Anomalies (Outliers) plot
   - Backtest (CrossValidation) plot
-  - Forecast plot
+  - Forecast plot

etna/models/__init__.py

@@ -8,6 +8,7 @@
 from etna.models.catboost import CatBoostModelPerSegment
 from etna.models.catboost import CatBoostMultiSegmentModel
 from etna.models.catboost import CatBoostPerSegmentModel
+from etna.models.deadline_ma import DeadlineMovingAverageModel
 from etna.models.holt_winters import HoltModel
 from etna.models.holt_winters import HoltWintersModel
 from etna.models.holt_winters import SimpleExpSmoothingModel

etna/models/deadline_ma.py

@@ -0,0 +1,169 @@
+import warnings
+from enum import Enum
+from typing import Dict
+from typing import List
+
+import numpy as np
+import pandas as pd
+
+from etna.models.base import PerSegmentModel
+
+
+class SeasonalityMode(Enum):
+    """Enum for seasonality mode for DeadlineMovingAverageModel."""
+
+    month = "month"
+    year = "year"
+
+    @classmethod
+    def _missing_(cls, value):
+        raise NotImplementedError(
+            f"{value} is not a valid {cls.__name__}. Only {', '.join([repr(m.value) for m in cls])} seasonality allowed"
+        )
+
+
+class _DeadlineMovingAverageModel:
+    """Moving average model that uses exact previous dates to predict."""
+
+    def __init__(self, window: int = 3, seasonality: str = "month"):
+        """
+        Initialize deadline moving average model.
+
+        Length of remembered tail of series is equal to the number of ``window`` months or years, depending on the ``seasonality``.
+
+        Parameters
+        ----------
+        window: int
+            Number of values taken for forecast for each point.
+        seasonality: str
+            Only allowed monthly or annual seasonality.
+        """
+        self.name = "target"
+        self.window = window
+        self.seasonality = SeasonalityMode(seasonality)
+        self.freqs_available = {"H", "D"}
+
+    def fit(self, df: pd.DataFrame, regressors: List[str]) -> "_DeadlineMovingAverageModel":
+        """
+        Fit DeadlineMovingAverageModel model.
+
+        Parameters
+        ----------
+        df: pd.DataFrame
+            Data to fit on
+        regressors:
+            List of the columns with regressors(ignored in this model)
+
+        Raises
+        ------
+        ValueError
+            If freq of dataframe is not supported
+        ValueError
+            If series is too short for chosen shift value
+
+        Returns
+        -------
+        :
+            Fitted model
+        """
+        freq = pd.infer_freq(df["timestamp"])
+        if freq not in self.freqs_available:
+            raise ValueError(f"{freq} is not supported! Use daily or hourly frequency!")
+
+        if set(df.columns) != {"timestamp", "target"}:
+            warnings.warn(
+                message=f"{type(self).__name__} does not work with any exogenous series or features. "
+                f"It uses only target series for predict/\n "
+            )
+        targets = df["target"]
+        timestamps = df["timestamp"]
+
+        if self.seasonality == SeasonalityMode.month:
+            first_index = timestamps.iloc[-1] - pd.DateOffset(months=self.window)
+
+        elif self.seasonality == SeasonalityMode.year:
+            first_index = timestamps.iloc[-1] - pd.DateOffset(years=self.window)
+
+        if first_index < timestamps.iloc[0]:
+            raise ValueError(
+                "Given series is too short for chosen shift value. Try lower shift value, or give" "longer series."
+            )
+
+        self.series = targets.loc[timestamps >= first_index]
+        self.timestamps = timestamps.loc[timestamps >= first_index]
+        self.shift = len(self.series)
+
+        return self
+
+    def predict(self, df: pd.DataFrame) -> np.ndarray:
+        """
+        Compute predictions from a DeadlineMovingAverageModel.
+
+        Parameters
+        ----------
+        df: pd.DataFrame
+            Used only for getting the horizon of forecast and timestamps.
+
+        Returns
+        -------
+        :
+            Array with predictions.
+        """
+        timestamps = df["timestamp"]
+        index = pd.date_range(start=self.timestamps.iloc[0], end=timestamps.iloc[-1])
+        res = np.append(self.series.values, np.zeros(len(df)))
+        res = pd.DataFrame(res)
+        res.index = index
+        for i in range(len(self.series), len(res)):
+            for w in range(1, self.window + 1):
+                if self.seasonality == SeasonalityMode.month:
+                    prev_date = res.index[i] - pd.DateOffset(months=w)
+
+                elif self.seasonality == SeasonalityMode.year:
+                    prev_date = res.index[i] - pd.DateOffset(years=w)
+                if prev_date <= self.timestamps.iloc[-1]:
+                    res.loc[index[i]] += self.series.loc[self.timestamps == prev_date].values
+                else:
+                    res.loc[index[i]] += res.loc[prev_date].values
+
+            res.loc[index[i]] = res.loc[index[i]] / self.window
+
+        res = res.values.reshape(
+            len(res),
+        )
+
+        return res[-len(df) :]
+
+
+class DeadlineMovingAverageModel(PerSegmentModel):
+    """Moving average model that uses exact previous dates to predict."""
+
+    def __init__(self, window: int = 3, seasonality: str = "month"):
+        """
+        Initialize deadline moving average model.
+
+        Parameters
+        ----------
+        window: int
+            Number of values taken for forecast for each point.
+        seasonality: str
+            Only allowed monthly or annual seasonality.
+        """
+        self.window = window
+        self.seasonality = seasonality
+        super(DeadlineMovingAverageModel, self).__init__(
+            base_model=_DeadlineMovingAverageModel(window=window, seasonality=seasonality)
+        )
+
+    def get_model(self) -> Dict[str, "DeadlineMovingAverageModel"]:
+        """Get internal model.
+
+        Returns
+        -------
+        :
+           Internal model
+        """
+        return self._get_model()
+
+
+__all__ = ["DeadlineMovingAverageModel"]