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Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization

Description

This repository contains the code accompanying the paper: Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization. It is implemented using PyTorch.

Setup

To set up just create a virtual environment with python3 and run:

pip install -r requirements.txt

Available

The code helps running ATR-CSPD, KCpE and RDR on both the generated dataset (section 4.2) and the NYC taxi timeseries (section 4.4).

Run an experiment

Run the file run.py

Example:
python3 run.py --dataset generated --model atrcspd
Usage:
usage: run.py [-h] [--model MODEL] [--dataset DATASET]

optional arguments:
-h, --help         show this help message and exit
--model MODEL      Model to run can be either: atrcspd, rulsif (RDR in the
                    paper) or kcpe.
--dataset DATASET  Dataset to run on, can be either: generated or nyc

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