Discussing the article: "Neural Networks in Trading: Dual-Attention-Based Trend Prediction Model"

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Check out the new article: Neural Networks in Trading: Dual-Attention-Based Trend Prediction Model.

We continue the discussion about the use of piecewise linear representation of time series, which was started in the previous article. Today we will see how to combine this method with other approaches to time series analysis to improve the price trend prediction quality.

A vast body of research has been dedicated to the prediction and analysis of financial time series. Traditional statistical methods often assume that time series are generated by linear processes, which limits their effectiveness in non-linear forecasting. Machine learning and deep learning methods have demonstrated greater success in modeling financial time series due to their ability to capture non-linear relationships. Many studies have focused on extracting features at specific time points and using them for modeling and prediction. However, such approaches often overlook data interactions and the short-term continuity of fluctuations.

To address these limitations, the study "A Dual-Attention-Based Stock Price Trend Prediction Model With Dual Features" proposes a dual-feature extraction method. This method leverages both individual time points and multiple temporal intervals. It integrates short-term market features with long-term temporal features to enhance prediction accuracy. The proposed model is based on an Encoder-Decoder architecture and employs attention mechanisms in both the Encoder and Decoder stages, enabling the identification of the most relevant features in long time series.

This research introduces a new Trend Prediction Model (TPM) designed to predict stock price trends by using dual-feature extraction and dual-attention mechanisms. The TPM aims to forecast both the direction and duration of stock price movements. The key contributions of the proposed approach are as follows:

  1. A novel dual-feature extraction method based on different time ranges, which effectively extracts important market information and optimizes forecasting results. TPM uses piecewise linear regression and a convolutional neural network to extract long-term and short-term market features from financial time series, respectively. The representation of market information through dual features significantly enhances the model's predictive performance.
  2. Stock price Trend Prediction Model (TPM) using the Encoder-Decoder structure and dual-attention mechanism. By adding attention mechanisms in both the Encoder and Decoder stages, TPM adaptively selects the most relevant short-term market features and combines them with long-term temporal features to improve forecasting accuracy.

Author: Dmitriy Gizlyk

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