Long short-term temporal fusion transformer for short-term forecasting of limit order book in China markets

Abstract

Short-term forecasting of the Limit Order Book (LOB) is challenging due to market noise. Traditionally, technical analysis using candlestick charts has been effective for market analysis and predictions. Inspired by this, we introduce a novel methodology. First, we preprocess the LOB data into long-term frame data resembling candlestick patterns to reduce noise interference. We then present the Long Short-Term Temporal Fusion Transformer (LSTFT), skillfully integrating both short-term and long-term information to capture complex dependencies and enhance prediction accuracy. Additionally, we propose a Temporal Attention Mechanism (TAM) that effectively distinguishes between long-term and short-term temporal relationships in LOB data. Our experimental results demonstrate the effectiveness of our approach in accurately forecasting the Limit Order Book in the short term.