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 and was inspired by Seanny123's repository.
To clone the repository please run:
git clone https://github.com/JulesBelveze/time-series-autoencoder.git Use uv
Then install uv
# install uv curl -LsSf https://astral.sh/uv/install.sh | sh # linux/mac # or brew install uv # mac with homebrewcd time-series-autoencoder uv venv uv pip sync pyproject.tomlInstall directly from requirements.txt
pip install -r requirements.txtThe project uses Hydra as a configuration parser. You can simply change the parameters directly within your .yaml file or you can override/set parameter using flags (for a complete guide please refer to the docs).
python3 main.py -cn=[PATH_TO_FOLDER_CONFIG] -cp=[CONFIG_NAME] 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 - handles multivariate time series
- attention mechanisms
- denoising autoencoder
- sparse autoencoder
You can find under the examples scripts to train the model in both cases:
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