基于Seasonal-Trend的时间序列预测（PyTorch完整源码和数据）

# LaST: Learning Latent Seasonal-Trend Representations for Time Series Forecasting  In this repository, we provide the original PyTorch implementation of LaST framework. [](https://drive.google.com/file/d/1LIi2OyHV0sqplMiw6l_aTjI5JhRQrYyZ/view?usp=sharing) ## Dataset We conducted extensive experiments on seven real-world benchmark datasets from four covering the categories of mainstream time series forecasting applications. Please download from the following buttons and place them into `datasets` folder. [](https://drive.google.com/drive/folders/13Ae_qDDxTQDroHCKUIG4xp3Sfi6yuhjX?usp=sharing) ## Usage #### Requirements The code was tested with `python 3.8`, `pytorch 1.8.1`, `cudatookkit 10.2`, and `cudnn 7.6.5`. Install the dependencies via [Anaconda](https://www.anaconda.com/): ```shell # create virtual environment conda create --name LaST python=3.8 # activate environment conda activate LaST # install pytorch & cudatoolkit conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts # install other requirements conda install numpy pandas ``` #### Run code To train and evaluate LaST framework on a dataset, run the following command: ```shell python run.py --data <dataset_name> --features <forecasting_mode> --seq_len <input_length> --pred_len <pred_length> --latent_size <latent_size> --batch_size <batch_size> --patience <patience> --seed <random_seed> ``` The detailed descriptions about the arguments are as following: | Parameter name | Description | | ---------------- | ------------------------------------------------------------ | | dataset_name | The dataset name can be selected from ["ETTh1", "ETTh2", "ETTm1", "ETTm2", "Exchange_rate", "Electricity", "Weather"] | | forecasting_mode | A value in ["S", "M"]. "S" denotes univariate forecasting while "M" denotes multivariate forecasting. | | input_length | The input (historical) sequence length, default is 201. | | pred_length | The output (forecasting) sequence length. | | latent_size | The dimension of latent representations, default is 128. | | batch_size | Batch size, default is 32. | | patience | The steps of early stop strategy in training. | | random_seed | The random seed. | ## Directory Structure The code directory structure is shown as follows: ```shell LaST ├── datasets # seven datasets files │ ├── ETTh1.csv │ ├── ETTh2.csv │ ├── ETTm1.csv │ ├── ETTm2.csv │ ├── electricity.csv │ ├── exchange_rate.csv │ └── weather.csv ├── expriments # training, validation, and test code of LaST │ ├── exp_basic.py │ └── exp_LaST.py ├── models # code of LaST and its dependencies │ ├── LaST.py # LaST main code │ └── utils.py # modules for LaST including autocorrelation, cort, etc. ├── utlis │ ├── data_loader.py # data loading and preprocessing code │ ├── metrics.py # metrics for evaluation │ ├── timefeatures.py # extract time-related features │ └── tools.py # tools for training, such as early stopping and learning rate controls ├── LICENSE # code license ├── run.py # entry for model training, validation, and test └── README.md # This file ``` ## Citation Please cite our paper if it's helpful to you in your research. ``` @inproceedings{wang2022latent, title = "Learning Latent Seasonal-Trend Representations for Time Series Forecasting", author = "Wang, Zhiyuan and Xu, Xovee and Zhang, Weifeng and Trajcevski, Goce and Zhong, Ting and Zhou, Fan", booktitle = "Advances in Neural Information Processing Systems", year = "2022" } ```