ruiluhuang / Time-series-prediction

A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats

Time series prediction

This repository implements the common methods of time series prediction, especially deep learning methods in TensorFlow2. It's welcomed to contribute if you have any better idea, just create a PR. If any question, feel free to open an issue.

Ongoing project, I will continue to improve this, so you might want to watch/star this repo to revisit.

Usage

1. Install the required library

$ pip install -r requirements.txt

2. Download the data, if necessary

$ sh ./data/download_passenger.sh

3. Train the model
   set custom_model_params if you want (refer to params in ./tfts/models/*.py), and pay attention to feature engineering.

$ cd examples
$ python run_train.py --use_model seq2seq
$ cd ..
$ tensorboard --logdir=./data/logs

4. Predict new data

$ cd examples
$ python run_test.py

Further reading

• https://github.com/awslabs/gluon-ts/
• https://github.com/Azure/DeepLearningForTimeSeriesForecasting
• https://github.com/microsoft/forecasting
• https://github.com/jdb78/pytorch-forecasting
• https://github.com/timeseriesAI/tsai