Get started with these basic examples:
- Time Series Prediction: Predict future values in a time series.
- Time Series Classification: Classify time series data into different categories.
- Time Series Anomaly Detection: Detect unusual patterns or anomalies in time series data.
- AutoML for parameters tuning: Automatically tune model parameters using Optuna.
Dive deeper with these notebooks:
- single step prediction: A guided example on predicting the next time point in weather data.
- multi steps prediction: Learn how to forecast multiple future time points in a sales dataset.
Check out these advanced examples and competition-winning implementations:
Multiple steps prediction
- TFTS-Bert wins the 3rd place in KDD Cup 2022 wind power forecasting
- TFTS-Seq2seq wins the 4th place in Tianchi ENSO prediction 2021
We welcome contributions! If you have an example, notebook, or improvement to share, please follow these steps