time-series-autoencoder:Pytorch双注意LSTM自动编码器，用于多元时间序列预测

<h1 align="center">LSTM-autoencoder with attentions for multivariate time series</h1> <p align="center"> <img src="https://hitcounter.pythonanywhere.com/count/tag.svg?url=https%3A%2F%2Fgithub.com%2FJulesBelveze%2Ftime-series-autoencoder" alt="Hits"> <img src="https://img.shields.io/badge/Made%20with-Python-1f425f.svg"> </p> This repository contains an autoencoder for multivariate time series forecasting. It features two attention mechanisms described in *[A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://arxiv.org/abs/1704.02971)* and was inspired by [Seanny123's repository](https://github.com/Seanny123/da-rnn).  ## Download and dependencies To clone the repository please run: ``` git clone https://github.com/JulesBelveze/time-series-autoencoder.git ``` To install all the required dependencies please run: ``` pip install -r requirements.txt ``` ## Usage ``` python main.py [-h] [--batch-size BATCH_SIZE] [--output-size OUTPUT_SIZE] [--label-col LABEL_COL] [--input-att INPUT_ATT] [--temporal-att TEMPORAL_ATT] [--seq-len SEQ_LEN] [--hidden-size-encoder HIDDEN_SIZE_ENCODER] [--hidden-size-decoder HIDDEN_SIZE_DECODER] [--reg-factor1 REG_FACTOR1] [--reg-factor2 REG_FACTOR2] [--reg1 REG1] [--reg2 REG2] [--denoising DENOISING] [--do-train DO_TRAIN] [--do-eval DO_EVAL] [--data-path DATA_PATH] [--output-dir OUTPUT_DIR] [--ckpt CKPT] ``` Optional arguments: ``` -h, --help show this help message and exit --batch-size BATCH_SIZE batch size --output-size OUTPUT_SIZE size of the ouput: default value to 1 for forecasting --label-col LABEL_COL name of the target column --input-att INPUT_ATT whether or not activate the input attention mechanism --temporal-att TEMPORAL_ATT whether or not activate the temporal attention mechanism --seq-len SEQ_LEN window length to use for forecasting --hidden-size-encoder HIDDEN_SIZE_ENCODER size of the encoder's hidden states --hidden-size-decoder HIDDEN_SIZE_DECODER size of the decoder's hidden states --reg-factor1 REG_FACTOR1 contribution factor of the L1 regularization if using a sparse autoencoder --reg-factor2 REG_FACTOR2 contribution factor of the L2 regularization if using a sparse autoencoder --reg1 REG1 activate/deactivate L1 regularization --reg2 REG2 activate/deactivate L2 regularization --denoising DENOISING whether or not to use a denoising autoencoder --do-train DO_TRAIN whether or not to train the model --do-eval DO_EVAL whether or not evaluating the mode --data-path DATA_PATH path to data file --output-dir OUTPUT_DIR name of folder to output files --ckpt CKPT checkpoint path for evaluation ``` ## Features * handles multivariate time series * attention mechanisms * denoising autoencoder * sparse autoencoder ## Examples You can find under the `examples` scripts to train the model in both cases: * reconstruction: the dataset can be found [here](https://gist.github.com/JulesBelveze/99ecdbea62f81ce647b131e7badbb24a) * forecasting: the dataset can be found [here](https://gist.github.com/JulesBelveze/e9997b9b0b68101029b461baf698bd72)