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https://github.com/hfawaz/inceptiontime

InceptionTime: Finding AlexNet for Time Series Classification
https://github.com/hfawaz/inceptiontime

alexnet deep-learning inception inceptionv3 neural-networks scalable time-series-analysis time-series-classification

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InceptionTime: Finding AlexNet for Time Series Classification

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# InceptionTime: Finding AlexNet for Time Series Classification

This is the companion repository for our paper titled [InceptionTime: Finding AlexNet for Time Series Classification](https://link.springer.com/article/10.1007/s10618-020-00710-y) published in [Data Mining and Knowledge Discovery](https://www.springer.com/journal/10618) and also available on [ArXiv](https://arxiv.org/pdf/1909.04939.pdf). 



## Inception module

![inception module](https://github.com/hfawaz/InceptionTime/blob/master/pngs/inception-module.png)



## Data

The data used in this project comes from the [UCR/UEA archive](http://timeseriesclassification.com/TSC.zip). 

We used the 85 datasets listed [here](https://www.cs.ucr.edu/~eamonn/time_series_data/).  



## Requirements

You will need to install the following packages present in the [requirements.txt](https://github.com/hfawaz/InceptionTime/blob/master/requirements.txt) file. 



## Code

The code is divided as follows: 

* The [main.py](https://github.com/hfawaz/InceptionTime/blob/master/main.py) python file contains the necessary code to run an experiement. 

* The [utils](https://github.com/hfawaz/InceptionTime/tree/master/utils) folder contains the necessary functions to read the datasets and visualize the plots.

* The [classifiers](https://github.com/hfawaz/InceptionTime/tree/master/classifiers) folder contains two python files: (1) [inception.py](https://github.com/hfawaz/InceptionTime/tree/master/classifiers/inception.py) contains the inception network; (2) [nne.py](https://github.com/hfawaz/InceptionTime/tree/master/classifiers/nne.py) contains the code that ensembles a set of Inception networks. 



### Adapt the code for your PC

You should first consider changing the following [line](https://github.com/hfawaz/InceptionTime/blob/c9a323c789984e3fb56e82ebb4eea6438611e59c/main.py#L83). 

This is the root file of everything (data and results) let's call it ```root_dir```. 



After that you should create a folder called ```archives``` inside your ```root_dir```, which should contain the folder ```UCR_TS_Archive_2015```. 

The latter will contain a folder for each dataset called ```dataset_name```, which can be downloaded from this [website](https://www.cs.ucr.edu/~eamonn/time_series_data/).



The names of the datasets are present [here](https://github.com/hfawaz/InceptionTime/blob/c9a323c789984e3fb56e82ebb4eea6438611e59c/utils/constants.py#L1). 

You can comment [this line](https://github.com/hfawaz/InceptionTime/blob/c9a323c789984e3fb56e82ebb4eea6438611e59c/utils/constants.py#L19) to run the experiments on all datasets. 



Once you have done all that, you can proceed to run on a single archive. 



### Run InceptionTime on a single Archive

You should issue the following command ```python3 main.py InceptionTime```. 



### Run the hyperparameter search for InceptionTime on a single Archive

You should issue the following command ```python3 main.py InceptionTime_xp```. 



### Run the length experiment on the InlineSkate dataset

You should first issue the following command ```python3 main.py run_length_xps``` to generate the resamples.

Then you should issue the following command ```python3 main.py InceptionTime``` but make sure that the ```InlineSkateXPs``` is chosen [here](https://github.com/hfawaz/InceptionTime/blob/690aa776081e77214db95ddd5c53c7ec3ac79d61/utils/constants.py#L22). 



### Receptive field

To run the experiments on the synthetic dataset, you should issue the following command ```python3 receptive.py```. 



## Results

The result (i.e. accuracy) for each dataset will be present in ```root_dir/results/nne/incepton-0-1-2-4-/UCR_TS_Archive_2015/dataset_name/df_metrics.csv```.



The raw results can be found [here](https://github.com/hfawaz/InceptionTime/blob/master/results-InceptionTime-85.csv) and generated using the following command ```python3 main.py generate_results_csv```.



We added the full results for the 128 datasets from the UCR archive, they can be found [here](https://github.com/hfawaz/InceptionTime/blob/master/results-InceptionTime-128.csv). 



The [results-inception-128.csv](https://github.com/hfawaz/InceptionTime/blob/master/results-inception-128.csv) file contains five individual runs of the Inception model over the 128 datasets from the UCR 2018 archive. 



### Critical difference diagrams

If you would like to generate such a diagram, take a look at [this code](https://github.com/hfawaz/cd-diagram)!



![cd diagram](https://github.com/hfawaz/InceptionTime/blob/master/pngs/cd-diagram.png)



### Training time plots

These plots were generated using the [matplotlib](https://matplotlib.org/) library. 



Training time vs train size             |  Training time vs series length

:-------------------------:|:-------------------------:

![training time size](https://github.com/hfawaz/InceptionTime/blob/master/pngs/train-time-size.png) | ![training time length](https://github.com/hfawaz/InceptionTime/blob/master/pngs/train-time-length.png)



### Receptive field

This plot was generated by issuing this command ```python3 receptive.py plot_results```.



Receptive field effect             |  Depth effect

:-------------------------:|:-------------------------:

![receptive field](https://github.com/hfawaz/InceptionTime/blob/master/pngs/plot-receptive-field.png) | ![training time length](https://github.com/hfawaz/InceptionTime/blob/master/pngs/depth-vs-length.png)



## Reference



If you re-use this work, please cite:



```

@article{IsmailFawaz2020inceptionTime,

  Title                    = {InceptionTime: Finding AlexNet for Time Series Classification},

  Author                   = {Ismail Fawaz, Hassan and Lucas, Benjamin and Forestier, Germain and Pelletier, Charlotte and Schmidt, Daniel F. and Weber, Jonathan and Webb, Geoffrey I. and Idoumghar, Lhassane and Muller, Pierre-Alain and Petitjean, François},

  journal                  = {Data Mining and Knowledge Discovery},

  Year                     = {2020}

}

```



## Acknowledgement



We would like to thank the providers of the [UCR/UEA archive](http://timeseriesclassification.com/TSC.zip). 

We would also like to thank NVIDIA Corporation for the Quadro P6000 grant and the Mésocentre of Strasbourg for providing access to the cluster.