Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks

TL;DR

This paper introduces a novel zero-shot energy price forecasting model using Kolmogorov-Arnold networks within a cross-domain adversarial framework, enhancing adaptability and accuracy across different energy markets.

Contribution

It presents a new cross-domain adaptation approach with Kolmogorov-Arnold networks for zero-shot time series forecasting in energy markets, improving generalization to unseen markets.

Findings

Outperforms baseline models in zero-shot electricity price prediction

Demonstrates improved capture of complex patterns in energy data

Enhances forecast robustness across diverse market conditions

Abstract

Accurate energy price forecasting is crucial for participants in day-ahead energy markets, as it significantly influences their decision-making processes. While machine learning-based approaches have shown promise in enhancing these forecasts, they often remain confined to the specific markets on which they are trained, thereby limiting their adaptability to new or unseen markets. In this paper, we introduce a cross-domain adaptation model designed to forecast energy prices by learning market-invariant representations across different markets during the training phase. We propose a doubly residual N-BEATS network with Kolmogorov Arnold networks at its core for time series forecasting. These networks, grounded in the Kolmogorov-Arnold representation theorem, offer a powerful way to approximate multivariate continuous functions. The cross domain adaptation model was generated with an adversarial framework. The model's effectiveness was tested in predicting day-ahead electricity prices in a zero shot fashion. In comparison with baseline models, our proposed framework shows promising results. By leveraging the Kolmogorov-Arnold networks, our model can potentially enhance its ability to capture complex patterns in energy price data, thus improving forecast accuracy across diverse market conditions. This addition not only enriches the model's representational capacity but also contributes to a more robust and flexible forecasting tool adaptable to various energy markets.