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03/31/2023, 3:58 AMMax (Nixtla)
03/31/2023, 6:16 PMKaustav Chaudhury
03/31/2023, 7:52 PMfede (nixtla) (they/them)
04/04/2023, 12:37 AMDistributedMLForecast
class and pass a spark dataframe to the fit
method, here’s an example: https://nixtla.github.io/mlforecast/distributed.forecast.html.
2. Our neuralforecast library is compatible with GPUs and multinode GPUs, but we are still working on making it compatible with spark. We haven’t tested it yet, but mlforecast also could work in such environments (through lightgbm). Unfortunately, StatsForecast currently does not support gpu.
3. Bringing your own models is perfectly possible using statsforecast (univariate models). We can help you with that if you are interested. MLForecast can also support custom models but in a distributed environment such as spark it could not be easy since the model needs to be distributed as well (for example, we use synapse ml to train lightgbm).
4. You can use your own (spark,dask, or ray) dataframe without a problem. The only requirement is to have at least three columns: unique_id, identifying the time series, ds
, identifying the temporal column, and y
, the target column. Here’s a description of the input dataset: https://nixtla.github.io/neuralforecast/examples/data_format.html.
Let us know how we can help you. :)Kaustav Chaudhury
04/04/2023, 5:06 AM