[timeseries] Update to GluonTS v0.13.0 + add DLinear and PatchTST models #3230
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| __all__ = [ | |||
| "DeepARModel", | |||
| "DLinearModel", | |||
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I am curious if there is a particular reason or quantitative analysis supporting the inclusion of these two models
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The main reason is supporting our commitment to provide an easy-to-use API for GluonTS models via TimeSeriesPredictor. This is the same reason why we still keep the MXNet models around, even though they are not used in any presets.
The second reason is that the papers introducing these two models demonstrated SoTA results on some forecasting tasks. Though, we need to do proper benchmarking before including these models to the presets.
| forecast_arrays.append(forecast.quantile(q)) | ||
| forecast_keys.append(str(q)) | ||
| # Compute all quantiles in parallel instead of a for-loop | ||
| quantiles = torch.tensor(quantile_levels, device=forecast.distribution.mean.device).reshape(-1, 1) |
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Any reason why we need to get the device from forecast distribution?
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If we don't do that, the quantiles tensor might reside on CPU while the forecast.distribution object is on GPU, which would lead to a device mismatch exception by PyTorch.
The code here is nearly identical to the code in DistributionForecast.quantile, except that we compute all the quantiles simultaneously in parallel, instead of doing a for loop.
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Description of changes:
Bump GluonTS dependency to v0.13.0.
Add two new models:
DLinearandPatchTST(not yet added to the presets).Clean up the logic for setting the default
context_lengthfor GluonTS models.Speed up the methods for converting
DistributionForecasttoQuantileForecastby vectorizing the code over allquantile_levels. Comparison of prediction time in seconds on M4 Quarterly (24000 time series) for SimpleFeedForward model that produces aDistributionForecast.The overall prediction time is still rather slow and probably could be further improved, but that would require digging into the GluonTS internals.
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