Robust Time Series Forecasting Using Transformer-Based Models for Volatile Market Conditions

Abstract

Accurately predicting Realised Downward Semi-Variance (RDS), a crucial metric for assessing the downside risk of prices for assets, may help traders steer their investment strategy and mitigate the effects of price decline-driven market volatility.  The availability of large data resources in several domains has made it feasible to use deep learning models with exceptionally high dimensionality that may represent long-term temporal and geographical context.  In the analysis of time series data, conventional techniques including Autoregressive Integrated Moving Averages (ARIMA), Long Short-Term Memory Networks (LSTM), Gated Recurrent Units (GRUs), and Recurrent Neural Networks (RNN) have offered reliable frameworks.  Due to their reliance on one-dimensional macroeconomic data, traditional prediction systems are difficult to adjust to complicated market developments.  This study examines the use of transformer models to energy market time-series forecasting, with a special emphasis on inter-provincial spot pricing.  The suggested approach outperforms more conventional models like ARIMA and LSTM in capturing long-range relationships by using the transformer's self-attention mechanism.  For stakeholders, such as utilities, legislators, and energy dealers, our study emphasises the need of increased forecasting accuracy.  To highlight its resilience, the suggested strategy is tested in a range of market circumstances, including times when the market is steady and times when it is turbulent.  The model's potential to optimise buying strategies and promote energy market stability is shown by the results, which show significant performance gains, especially in turbulent markets.