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## TL;DR
- It is hard to encode/decode batches of time series in MLServer
- An idiomatic example would be helpful
- An new content type and/or inference runtime could help even more
## Descripti…
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### Description
[Currently](https://github.com/Nixtla/statsforecast/blob/main/statsforecast/core.py#L240-L244) the models' fit during forecasting and crossvalidation is lost. Would be nice to have …
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reference: https://machinelearningmastery.com/how-to-develop-lstm-models-for-multi-step-time-series-forecasting-of-household-power-consumption/
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**Describe the bug**
StatsForecastAutoARIMA does not seem to use multiple cores/jobs.
For my dataset with 2000 panel, monthly time series the Nixtla version takes around 2 seconds but minutes for…
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Test the model and evaluate MAPE
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Test the model and evaluate MAPE
https://unit8.com/resources/darts-time-series-made-easy-in-python/
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- [ ] ARIMA
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1. How to select an ideal forecasting method? (~ Ward et al. 2014)
* evaluate forecasting approaches across different time series
* do time series properties influence which methods (or para…
ha0ye updated
5 years ago
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- PyTorch-Forecasting version: 1.0
- PyTorch version:
- Python version:
- Operating System:
### Expected behavior
I added lags to my timeseriesdataset and added the variables to time_varying_un…
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Has any work been done with state space models. I'd be curious how they would perform with this framework applied.