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Currently, tsfeatures utilizes a map-reduce approach and multiprocessing to compute several features for different time series. However, the implementation is currently only supported for pandas. By incorporating fugue, we can ensure tsfeatures compatibility with spark, ray, and dask.
Description
Currently,
tsfeatures
utilizes a map-reduce approach and multiprocessing to compute several features for different time series. However, the implementation is currently only supported for pandas. By incorporating fugue, we can ensuretsfeatures
compatibility withspark
,ray
, anddask
.For reference on how the implementation should look, please see https://github.com/Nixtla/statsforecast/blob/main/statsforecast/core.py#L1784.
Use case
No response
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