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SSIM_Seq2Seq

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SSIM - A Deep Learning Approach for Recovering Missing Time Series Sensor Data

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# SSIM Model

This is the SSIM model for *[SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data](https://ieeexplore.ieee.org/document/8681112)*

Considering the dataset we are using in the paper is not public available, we use a different open dataset for demo.

The original PM2.5 data can be download from: [PM2.5](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)

The Pytorch implementation has not been fully tested.
Bugs may be fixed later.

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Update 2021

>Datasets from the same WQ monitoring stations can be found on Kaggle as [Real Time Water Quality Data](https://www.kaggle.com/ivivan/real-time-water-quality-data). The measurements may be different from the paper because of sensor replacement.

>Please check our improved SSIM model. The paper *"A Dual-Head Attention Model for Time Series Data Imputation" has been submitted to "computers and electronics in agriculture"*. The code can be found in repo [DualHeadSSIM](https://github.com/ivivan/DualHeadSSIM).

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Code structure:

/checkpoints ------- store trained model

/data        ------- data set

/model       ------- SSIM model: encoder, decoder, attention

/utils

/prepare_PM2.5 ------------ prepare train/test for PM2.5 data. 2010-2013 for train, 2014 for test

/VLSM --------------- VLSM algorithm to generate variable length samples (with 0 pad)

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Three branches:
+ master: PyTorch version
+ newest: PyTorch version (same model as master, training functions slightly changed for other papers )
+ MXnet: MXnet version

***
Link the model's architecture with the equations in the paper

![Explaination](https://raw.githubusercontent.com/ivivan/SSIM_Seq2Seq/newtest/Model.png "SSIM")

SSIM_Seq2Seq

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