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Add urban_meal_delivery.forecasts.models sub-package

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- `*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
This commit is contained in:
Alexander Hess 2021-02-01 20:39:52 +01:00
commit 67cd58cf16
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
12 changed files with 747 additions and 71 deletions
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10
tests/config.py
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@ -11,10 +11,10 @@ YEAR, MONTH, DAY = 2016, 7, 1
# The hour when most test cases take place.
NOON = 12
# `START` and `END` constitute a 15-day time span.
# That implies a maximum `train_horizon` of `2` as that needs full 7-day weeks.
# `START` and `END` constitute a 22-day time span.
# That implies a maximum `train_horizon` of `3` as that needs full 7-day weeks.
START = datetime.datetime(YEAR, MONTH, DAY, config.SERVICE_START, 0)
END = datetime.datetime(YEAR, MONTH, 15, config.SERVICE_END, 0)
END = datetime.datetime(YEAR, MONTH, DAY + 21, config.SERVICE_END, 0)
# Default time steps (in minutes), for example, for `OrderHistory` objects.
LONG_TIME_STEP = 60
@ -28,6 +28,6 @@ VERTICAL_FREQUENCY_SHORT = 7 * 24
# Default training horizons, for example, for
# `OrderHistory.make_horizontal_time_series()`.
LONG_TRAIN_HORIZON = 2
SHORT_TRAIN_HORIZON = 1
LONG_TRAIN_HORIZON = 3
SHORT_TRAIN_HORIZON = 2
TRAIN_HORIZONS = (SHORT_TRAIN_HORIZON, LONG_TRAIN_HORIZON)