The article delves into advancements in multivariate time series forecasting with a focus on the LSEAttention framework. This approach addresses numerical instability in traditional Transformers used in long-term forecasting tasks, which are prone to issues like attention and entropy collapse. The integration of Log-Sum-Exp (LSE) and GELU activation functions within this framework enhances numerical stability and mitigates abrupt transitions in attention scores, ensuring a balanced distribution of attention across input sequences. Furthermore, the implementation aspects of LSEAttention in MQL5, including modifications to Softmax layers and Relative Attention modules, underscore the practical enhancements made to bolster forecasting accuracy and efficiency.

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Multivariate time series forecasting is a machine learning task focusing on predicting future trends from historical data. This task is difficult due to feature correlations and temporal dependencies and finds real-world applications in sectors like healthcare and finance. Transformer-based architectures, although impactful in NLP and computer vision, face challenges in time series forecasting due to training instability, especially with smaller datasets. The "SAMformer" framework addresses this by using simplified architecture, incorporating Sharpness-Aware Minimization and channel-wise attention to improve training stability and generalization. SAMformer optimizes Transformers to perform competitively by tackling entropy and loss sharpness issues, introducing novel strategies to enhance model efficiency and reliability.

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#MQL5 #MT5 #TimeSeries

The Multitask-Stockformer framework is detailed in a multi-part analysis of its theoretical and practical aspects, focusing on MQL5 implementation. It integrates discrete wavelet transformation for time series analysis with multitask self-attention models to capture complex financial data dependencies. The framework consists of three core modules: time series decomposition, a dual-frequency spatio-temporal encoder, and a dual-frequency fusion decoder. Each module enhances the analysis and prediction accuracy by focusing on different frequency components. The system is designed to handle diverse market conditions effectively, providing trend analysis, anomaly detection, and dynamic market adaptability. Implementation efforts continue with key system components optimized for time series analysis.

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The Hidformer framework leverages a unique dual-tower encoder structure to effectively analyze and forecast complex multivariate time series, particularly beneficial for handling dynamic and volatile data. This framework excels in drawing out both explicit and hidden dependencies in the data through advanced attention mechanisms, enhancing the analysis of both temporal structures and frequency domains.

A significant feature of Hidformer is its recursive attention mechanism, which aids in capturing intricate temporal dependencies in financial data. The linear attention mechanism complements this by optimizing computations while maintaining training stability. Together, these components enable reliable forecasts, vital in high-volatility markets.

The model's multilayer perceptron-based decoder offers efficient sequence prediction, improving long-t...

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#MQL5 #MT5 #TimeSeries