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https://github.com/julesbelveze/time-series-autoencoder
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
https://github.com/julesbelveze/time-series-autoencoder
attention-mechanisms autoencoder forecasting lstm-autoencoder multivariate-timeseries pytorch time-series
Last synced: 25 days ago
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PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
- Host: GitHub
- URL: https://github.com/julesbelveze/time-series-autoencoder
- Owner: JulesBelveze
- License: apache-2.0
- Created: 2020-06-05T09:31:36.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-27T13:14:49.000Z (4 months ago)
- Last Synced: 2024-10-03T10:34:23.948Z (about 1 month ago)
- Topics: attention-mechanisms, autoencoder, forecasting, lstm-autoencoder, multivariate-timeseries, pytorch, time-series
- Language: Python
- Homepage:
- Size: 391 KB
- Stars: 630
- Watchers: 6
- Forks: 62
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
LSTM-autoencoder with attentions for multivariate time series
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:
```
python3 -m venv .venv/tsa
source .venv/tsa/bin/activate
poetry install
```
## Usage
The project uses [Hydra](https://hydra.cc/docs/intro/) 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
```
## 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)