mlw67 / deep-time-series

Deep learning PyTorch library for time series forecasting

Deep Learning for Time Series forecasting

  This repo included a collection of models (transformers, attention models, GRUs) mainly focuses on the progress of time series forecasting using deep learning. It was originally collected for financial market forecasting, which has been organized into a unified framework for easier use.

  For beginners, we recommend you read this paper or the brief introduction we provided to learn about time series forecasting. And read the paper for a more comprehensive understanding.

 With limited ability and energy, we only test how well the repo works on a specific dataset. For problems in use, please leave an issue. The repo will be kept updated.

 See the dev branch for recent updates or the master branch not works.

Requirements

• Python 3.7
• matplotlib == 3.1.1
• numpy == 1.21.5
• pandas == 0.25.1
• scikit_learn == 0.21.3
• torch == 1.7.1

Dependencies can be installed using the following command:

pip install -r requirements.txt

Get Started

1. Download data provided by the repo. You can obtain all the six benchmarks from Tsinghua Cloud or Google Drive. All the datasets are well pre-processed and can be used easily.
2. Train the model and predict.
   • Step1. set model e.g. "autoformer".
   • Step2. set dataset, i.e. assign a Dataset class to feed data into the pre-determined model.
   • Step3. set some essential params included data_path, file_name,seq_len,label_len,pred_len,features,T,M,S,MS. Default params we has provided can be set from data_parser if data is provided.
   • Other. Sometimes it is necessary to revise _process_one_batch and _get_data in exp_main.

A simple command included three parameters correspond to the above three steps:

python -u main.py --model 'autoformer' --dataset 'ETTh1' --data "ETTh1"

Usage on customized data(comming soon)

 To run on your customized data, a DataSet class must be provided in data_loader.py, then add the Dataset to Exp_Basic.py. Need to be noted that elements ejected from the DataSet class must conform to the model's requirement.

See Colab Examples for detail: We provide google colabs to help reproducing and customing our repo, which includes experiments(train and test), forecasting, visualization and custom data.

Simple Results on ETT

Ses the repo for more details on ETT dataset.

Autoformer result for the task (only 2 epoches for train, test 300+ samples):

Figure 1. Autoformer results (randomly choose a sample).

Figure 2. Autoformer results (values distribution).

Simple Results on Oze Challenge

This challenge aims at introducing a new statistical model to predict and analyze energy consumptions and temperatures in a big building using observations stored in the Oze-Energies database. More details can be seen from the repo. A simple model(Seq2Seq) result for the task (only 2 epoches for train, test 2 samples):

Figure 3. GRU results (randomly choose a sample).

Figure 4. GRU results (values distribution).

Models currently supported

We will keep adding series forecasting models to expand this repo.

Year	Model name	Models	Tasks
---	LSTM	Vanilla Lstm	many to one
2014	EDLstm	Sequence to Sequence Learning with Neural Networks	many to many
2014	EDGru	Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation	many to many
2016	EDGruAtt	Neural Machine Translation by Jointly Learning to Align and Translate	many to many
2017	Transformer	Attention Is All You Need	many to many
2017	DeepAR	DeepAR:Probabilistic Forecasting with Autoregressive Recurrent Networks	many to many
2017	LSTNet	Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks	many-to-one
2018	TPA	TPA:Temporal Pattern Attention for Multivariate Time Series Forecasting	many to one
2018	TCN	TCN:An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling	many to one
2019	LogTrans	LogTrans:Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting	many to many
2020	Informer	Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting	many to many
2021	Autoformer	Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting	many to many
	Reformer	Reformer	many to many
	Transformer XL	Transformer XL	many to many
-2019	N-BEATS	N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

DOING and TODO

1. Add probability estimation function.
2. Improve the network structure(especially attention network) according to our data scenario.
3. Add Tensorboard to record exp.

About exp_single and exp_multi

Usually we will encounter three forms of data:

1. multi files(usual caused by multi individual) which will cause oom if load all of them. Every separate file contaies train, vail, test.
2. sigle file contaied train, vail, test.
3. multi separate files (usual three) i.e. train, vail, test.

 For 1, we load a file (train, vail, test dataset) iteratively in a epoch until all files are loaded and fed to a model. i.e.exp_multi.py. For 2, 3, we load train, vail, test dataset before starting training. i.e. exp_single.py.

Contact

If you have any questions, feel free to contact hyliu through Email ([e-mail]) or Github issues.

Acknowlegements

To complete the project, we referenced the following repos. Informer2020, AdjustAutocorrelation, flow-forecast, pytorch-seq2seq, Autoformer.