Implemented moving average #926
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added 2 commits
July 14, 2020 18:11
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Could we add some basic tests for this? Something like sanity checks, shape checks, trivial input/output and maybe edge cases (NaNs, empty sequence, length 1 sequence, etc.)? Maybe under |
jaheba
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Jul 16, 2020
jaheba
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Jul 21, 2020
jaheba
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Jul 23, 2020
jaheba
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Jul 23, 2020
jaheba
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Jul 27, 2020
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| ds = ListDataset([{"target": target, "start": start}], freq=freq) | ||
| item = next(iter(ds)) | ||
| predictions = mp.predict_item(item).samples[0] |
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| predictions = mp.predict_item(item).samples[0] | |
| predictions = mp.predict_item(item).mean |
I like this a bit better.
jaheba
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Jul 27, 2020
src/gluonts/model/trivial/mean.py
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| # assert ( | ||
| # context_length >= 1 | ||
| # ), "The value of `context_length` should be >= 1" |
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We should still have a check when context_length is an integer.
src/gluonts/model/trivial/mean.py
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| start_date = forecast_start(item) | ||
| return SampleForecast( | ||
| samples=np.array([target[-self.prediction_length :]]), | ||
| start_date=start_date, | ||
| freq=self.freq, | ||
| item_id=item.get(FieldName.ITEM_ID), | ||
| ) |
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| start_date = forecast_start(item) | |
| return SampleForecast( | |
| samples=np.array([target[-self.prediction_length :]]), | |
| start_date=start_date, | |
| freq=self.freq, | |
| item_id=item.get(FieldName.ITEM_ID), | |
| ) | |
| return SampleForecast( | |
| samples=np.array([target[-self.prediction_length :]]), | |
| start_date=forecast_start(item), | |
| freq=self.freq, | |
| item_id=item.get(FieldName.ITEM_ID), | |
| ) |
jaheba
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Jul 27, 2020
kashif
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Oct 10, 2020
* Add moving average * Add moving average * Add moving average - added tests * context_length now optional::forecast_start is now used::updated tests * refinments added * added assert + forecast_start updates * forecast_start updates * forecast_start updates + assert Co-authored-by: Pedro Eduardo Mercado Lopez <pedroml@amazon.com>
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Issue #, if available:
Description of changes:
-- This is a deterministic task (no distribution of observations is assumed)
-- For the case where prediction_length = 1, the output is the standard moving average of the previous context_length observations
-- For the case where prediction_length > 1, the input target is appended with previous moving averages, further computing moving averages until prediction_length of them are computed.
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