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
29 lines
1.5 KiB
Python
29 lines
1.5 KiB
Python
"""Demand forecasting utilities.
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This sub-package is divided into further sub-packages and modules as follows:
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`methods` contains various time series related statistical methods, implemented
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as plain `function` objects that are used to predict into the future given a
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time series of historic order counts. The methods are context-agnostic, meaning
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that they only take and return `pd.Series/DataFrame`s holding numbers and
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are not concerned with how these numbers were generated or what they mean.
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Some functions, like `arima.predict()` or `ets.predict()` wrap functions called
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in R using the `rpy2` library. Others, like `extrapolate_season.predict()`, are
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written in plain Python.
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`timify` defines an `OrderHistory` class that abstracts away the communication
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with the database and provides `pd.Series` objects with the order counts that
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are fed into the `methods`. In particular, it uses SQL statements behind the
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scenes to calculate the historic order counts on a per-`Pixel` level. Once the
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data is loaded from the database, an `OrderHistory` instance provides various
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ways to slice out, or generate, different kinds of order time series (e.g.,
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"horizontal" vs. "vertical" time series).
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`models` defines various forecasting `*Model`s that combine a given kind of
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time series with one of the forecasting `methods`. For example, the ETS method
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applied to a horizontal time series is implemented in the `HorizontalETSModel`.
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"""
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from urban_meal_delivery.forecasts import methods
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from urban_meal_delivery.forecasts import models
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from urban_meal_delivery.forecasts import timify
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