FilterTS

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The code for FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting

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# FilterTS
The code for **FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting**

![FilterTS main](https://raw.githubusercontent.com/wyl010607/FilterTS/main/pic/FilterTS_main.png)

## Get Started
### Installation
Please install the corresponding version of PyTorch via the official PyTorch website, then follow these steps to set up your environment.
```bash
pip install -r requirements.txt
```

### Download the Dataset
Download the dataset from the following [link](https://drive.google.com/file/d/1l51QsKvQPcqILT3DwfjCgx8Dsg2rpjot/edit) and place it in the `./dataset/` directory.
(Please note, this dataset download link is provided by the author of iTransformer and is not associated with any author's address)

## Training Scripts
Run the shell script in the `./scripts` folder, using the following command, for example:
```bash
sh ./scripts/FilterTS_electricity.sh
```

## Main Experimental Results
![Main Results](https://raw.githubusercontent.com/wyl010607/FilterTS/main/pic/main_results.png)

FilterTS demonstrates superior accuracy across eight multivariate long-term time series forecasting datasets compared to current state-of-the-art models in most cases.

## Note on the drop_last Setting

During a recent code review we discovered that an unintended drop_last=True option was left active in the dataloader.
Below are the results obtained after rerunning all experiments with drop_last=False. Across the majority of settings the differences are negligible, confirming that the original conclusions remain valid.

[Main Results](./pic/main_results_fix.png)

FilterTS

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