Alexander Hess
b8952213d8
- the function implements a forecasting "method" similar to the
seasonal naive method
=> instead of simply taking the last observation given a seasonal lag,
it linearly extrapolates all observations of the same seasonal lag
from the past into the future; conceptually, it is like the
seasonal naive method with built-in smoothing
- the function is tested just like the `arima.predict()` and
`ets.predict()` functions
+ rename the `tests.forecasts.methods.test_ts_methods` module
into `tests.forecasts.methods.test_predictions`
- re-organize some constants in the `tests` package
- streamline some docstrings
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
"""Globals used when testing."""
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import datetime
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from urban_meal_delivery import config
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# The day on which most test cases take place.
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YEAR, MONTH, DAY = 2016, 7, 1
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# The hour when most test cases take place.
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NOON = 12
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# `START` and `END` constitute a 15-day time span.
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# That implies a maximum `train_horizon` of `2` as that needs full 7-day weeks.
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START = datetime.datetime(YEAR, MONTH, DAY, config.SERVICE_START, 0)
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END = datetime.datetime(YEAR, MONTH, 15, config.SERVICE_END, 0)
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# Default time steps (in minutes), for example, for `OrderHistory` objects.
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LONG_TIME_STEP = 60
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SHORT_TIME_STEP = 30
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TIME_STEPS = (SHORT_TIME_STEP, LONG_TIME_STEP)
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# The `frequency` of vertical time series is the number of days in a week, 7,
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# times the number of time steps per day. With 12 operating hours (11 am - 11 pm)
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# the `frequency`s are 84 and 168 for the `LONG/SHORT_TIME_STEP`s.
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VERTICAL_FREQUENCY_LONG = 7 * 12
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VERTICAL_FREQUENCY_SHORT = 7 * 24
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# Default training horizons, for example, for
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# `OrderHistory.make_horizontal_time_series()`.
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LONG_TRAIN_HORIZON = 2
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SHORT_TRAIN_HORIZON = 1
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TRAIN_HORIZONS = (SHORT_TRAIN_HORIZON, LONG_TRAIN_HORIZON)
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