Time-series forecasting

 

These tutorials implement an end-to-end time-series application including:

• Distributed data preprocessing and model training: Ingest and preprocess data at scale using Ray Data. Then, train a distributed DLinear model using Ray Train.

• Model validation using offline inference: Evaluate the model using Ray Data offline batch inference.

• Online model serving: Deploy the model as a scalable online service using Ray Serve.

• Production deployment: Create production batch Jobs for offline workloads including data prep, training, batch prediction, and potentially online Services.

Setup

Run the following:

pip install -r requirements.txt && pip install -e .

Acknowledgements

This repository is based on the official DLinear implementations:

• DLinear
• LTSF-Linear

And the original publication:

• "Are Transformers Effective for Time Series Forecasting?"

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e2e_timeseries/01-Distributed-Training
e2e_timeseries/02-Validation
e2e_timeseries/03-Serving