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* add LSTNet example * adding apache licence header
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# Implementation | ||
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This tutorial shows how to implement LSTNet, a multivariate time series forecasting model submitted by Wei-Cheng Chang, Yiming Yang, Hanxiao Liu and Guokun Lai in their paper [Modeling Long- and Short-Term Temporal Patterns](https://arxiv.org/pdf/1703.07015.pdf) in March 2017. This model achieved state of the art performance on 3 of the 4 public datasets it was evaluated on. | ||
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## Running the code | ||
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1. Download and unpack the public electricity dataset used in the paper. This dataset comprises measurements of electricity consumption in kWh every hour from 2012 to 2014 for 321 different clients. | ||
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```s | ||
$ wget https://github.com/laiguokun/multivariate-time-series-data/raw/master/electricity/electricity.txt.gz | ||
$ gunzip electricity.txt.gz | ||
``` | ||
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2. preprocess data with `python preprocess.py` | ||
3. set model hyperparameters in `/src/config.py` | ||
4. `python train.py` |
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