MSIG-main

Multi-Scale Information Granule-Based Time Series Forecasting Model with Two-Stage Prediction Mechanism

Requirements

The model is implemented using Python 3.9 with dependencies specified in requirements.txt

Data Preparation

time series datasets

Download Traffic, Electricity, Exchange-rate datasets from https://github.com/laiguokun/multivariate-time-series-data. Uncompress them and move them to the data folder.

Setup

1. Create conda environment(Optional)

conda create -n basisformer -y python=3.9 
conda activate MSIG

2. Install dependecies

Install the required packages

pip install -r requirements.txt

3. Download the data

We follow the same setting as previous work. The datasets for all the six benchmarks can be obtained from [Autoformer]. The datasets are placed in the 'all_six_datasets' folder of our project. The tree structure of the files are as follows:

MSIG\data\ETT
│
├─ECL
│
├─ETTh1
│
├─EXCHG
│
├─TRFC
│
├─ETTm2
│
├─BJPM5
│
├─SYPM5
│
└─SHPM5
│
└─covid_19

4. Experimental setup

The length of the historical input sequence is maintained at $96$, whereas the length of the sequence to be predicted is selected from a range of values, i.e., ${96, 192, 336, 720}$. Note that the input length is fixed to be 96 for all methods for a fair comparison. For ETTh2, ETTm2, ECL, TRFC, EXCHG, Covid_19 data sets, the evaluation is based on mean square error (MSE) and mean absolute error (MAE) measures. Other data sets are measured by RMSE, SMAPE, and MASEL.

Main Results

1. ETTh1 and ETTm2 prediction results

2. ECL, EXCHG and TRFC prediction results

Train and Evaluates

1. Univariate forecasting

sh script/MSIG.sh

Contact

If there are any issues, please ask in the GitHub Issue module

Acknowledgement

We appreciate the following github repos a lot for their valuable code base or datasets:

https://github.com/MAZiqing/FEDformer

https://github.com/thuml/Autoformer

https://github.com/zhouhaoyi/Informer2020

https://github.com/zhouhaoyi/ETDataset

https://github.com/laiguokun/multivariate-time-series-data