Alexander Hess
67cd58cf16
- `*Model`s use the `methods.*.predict()` functions to predict demand given an order time series generated by `timify.OrderHistory` - `models.base.ForecastingModelABC` unifies how all `*Model`s work and implements a caching strategy - implement three `*Model`s for tactical forecasting, based on the hets, varima, and rtarima models described in the first research paper - add overall documentation for `urban_meal_delivery.forecasts` package - move the fixtures in `tests.forecasts.timify.conftest` to `tests.forecasts.conftest` and adjust the horizon of the test horizon from two to three weeks
119 lines
4.8 KiB
Python
119 lines
4.8 KiB
Python
"""Vertical forecasting `*Model`s to predict demand for tactical purposes.
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Vertical `*Model`s take order counts of all time steps in the training data
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and make a prediction for all time steps on the day to be predicted at once.
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Thus, the training time series have a `frequency` of the number of weekdays,
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`7`, times the number of time steps on a day. For example, with 60-minute time
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steps, the `frequency` becomes `7 * 12` (= operating hours from 11 am to 11 pm),
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which is `84`. Vertical `*Model`s train the forecasting `methods` on a seasonally
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decomposed time series internally.
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""" # noqa:RST215
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import datetime as dt
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import pandas as pd
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from urban_meal_delivery import db
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from urban_meal_delivery.forecasts import methods
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from urban_meal_delivery.forecasts.models import base
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class VerticalARIMAModel(base.ForecastingModelABC):
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"""The ARIMA model applied on a vertical time series."""
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name = 'varima'
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def predict(
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self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
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) -> pd.DataFrame:
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"""Predict demand for a time step.
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Args:
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pixel: pixel in which the prediction is made
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predict_at: time step (i.e., "start_at") to make the prediction for
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train_horizon: weeks of historic data used to predict `predict_at`
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Returns:
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actual order counts (i.e., the "actual" column),
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point forecasts (i.e., the "prediction" column), and
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confidence intervals (i.e, the four "low/high/80/95" columns);
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contains several rows, including one for the `predict_at` time step
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# noqa:DAR401 RuntimeError
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"""
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# Generate the historic (and vertical) order time series.
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training_ts, frequency, actuals_ts = self._order_history.make_vertical_ts(
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pixel_id=pixel.id,
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predict_day=predict_at.date(),
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train_horizon=train_horizon,
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)
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# Decompose the `training_ts` to make predictions for the seasonal
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# component and the seasonally adjusted observations separately.
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decomposed_training_ts = methods.decomposition.stl(
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time_series=training_ts,
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frequency=frequency,
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# "Periodic" `ns` parameter => same seasonal component value
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# for observations of the same lag.
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ns=999,
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)
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# Make predictions for the seasonal component by linear extrapolation.
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seasonal_predictions = methods.extrapolate_season.predict(
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training_ts=decomposed_training_ts['seasonal'],
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forecast_interval=actuals_ts.index,
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frequency=frequency,
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)
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# Make predictions with the ARIMA model on the seasonally adjusted time series.
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seasonally_adjusted_predictions = methods.arima.predict(
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training_ts=(
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decomposed_training_ts['trend'] + decomposed_training_ts['residual']
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),
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forecast_interval=actuals_ts.index,
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# Because the seasonality was taken out before,
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# the `training_ts` has, by definition, a `frequency` of `1`.
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frequency=1,
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seasonal_fit=False,
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)
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# The overall `predictions` are the sum of the separate predictions above.
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# As the linear extrapolation of the seasonal component has no
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# confidence interval, we put the one from the ARIMA model around
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# the extrapolated seasonal component.
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predictions = pd.DataFrame(
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data={
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'actual': actuals_ts,
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'prediction': (
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seasonal_predictions['prediction'] # noqa:WPS204
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+ seasonally_adjusted_predictions['prediction']
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),
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'low80': (
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seasonal_predictions['prediction']
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+ seasonally_adjusted_predictions['low80']
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),
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'high80': (
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seasonal_predictions['prediction']
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+ seasonally_adjusted_predictions['high80']
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),
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'low95': (
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seasonal_predictions['prediction']
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+ seasonally_adjusted_predictions['low95']
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),
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'high95': (
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seasonal_predictions['prediction']
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+ seasonally_adjusted_predictions['high95']
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),
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},
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index=actuals_ts.index,
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)
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# Sanity checks.
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if len(predictions) <= 1: # pragma: no cover
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raise RuntimeError('vertical models should predict several time steps')
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if predictions.isnull().any().any(): # pragma: no cover
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raise RuntimeError('missing predictions in varima model')
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if predict_at not in predictions.index: # pragma: no cover
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raise RuntimeError('missing prediction for `predict_at`')
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return predictions
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