# Woa-wtconv-kanformer for long term time series forecasting

## TL;DR

This paper introduces a new model for long-term time series forecasting that improves performance and training efficiency by combining wavelet processing, hyperparameter optimization, and a modified linear layer.

## Contribution

The WOA-WTConv-KANformer model introduces wavelet-based feature extraction, whale optimization for hyperparameters, and a KAN module to enhance long-term forecasting.

## Key findings

- The model achieves performance improvements across different prediction lengths.
- Training efficiency is enhanced compared to traditional transformer-based models.
- The model shows potential for real-world time series prediction applications.

## Abstract

Multivariate time series analysis and prediction are of great significance in traffic management, weather forecasting and other practical applications. However, most of the existing research focuses on using the traditional transformer model as the framework to predict short series or predict with time domain features, and the effect of removing noise interference with irregular frequency is not good. At the same time, because the model based on the transformer framework needs to adjust too many hyperparameters, improper parameter design in practical use will lead to model performance degradation, so we propose the WOA-WTConv-KANformer model. The model is optimized based on the itransformer time series prediction model. Before embedding the time nodes of each series into the variable token and input into the encoder layer, the WTConv2d model is used to process the data with wavelet frequency to extract the frequency domain and time domain features. So that the model can solve the non-stationarity problem of time series data caused by the frequency domain problem. In order to realize the effective training of the model, we also use the whale optimization algorithm to make the model reasonably adjust the hyperparameters before formal training. At the same time, the KAN module is used as the linear layer instead of MLP during the training and use of the model, so that the model can improve the performance of different prediction lengths. The number of training parameters is also reduced. Through five public prediction datasets, it is shown that our model can achieve performance improvement on different prediction lengths, and the training efficiency is also improved, which proves the potential of the model in the field of real-world time series prediction.