The era of information explosion had prompted the accumulation of a tremendous amount of time-series data, including stationary and non-stationary time-series data. State-of-the-art algorithms have achieved a decent performance in dealing with stationary temporal data. However, traditional algorithms that tackle stationary time-series do not apply to non-stationary series like Forex trading. This paper investigates applicable models that can improve the accuracy of forecasting future trends of non-stationary time-series sequences. In particular, we focus on identifying potential models and investigate the effects of recognizing patterns from historical data. We propose a combination of \rebuttal{the} seq2seq model based on RNN, along with an attention mechanism and an enriched set features extracted via dynamic time warping and zigzag peak valley indicators. Customized loss functions and evaluating metrics have been designed to focus more on the predicting sequence's peaks and valley points. Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making. We further provide evaluations of the effects of various loss functions, evaluation metrics, model variants, and components on model performance.

翻译：信息爆炸时代促使大量时间序列数据积累,包括固定和非静止时间序列数据。最新算法在处理固定时间数据方面取得了体面的业绩。然而,处理固定时间序列的传统算法不适用于Forex交易等非静止系列。本文调查了能够提高预测非静止时间序列序列序列未来趋势准确性的适用模型。特别是,我们侧重于确定潜在模型,并调查从历史数据中识别模式的影响。我们提议将基于RNN的\butal{the}后继2seq模型与关注机制和通过动态时间扭曲和zigzag峰谷指标提取的丰富特征相结合。定制的损失函数和评估指标旨在更加注重预测非静止时间序列峰点和谷点的预测。我们的结果显示,我们的模型可以预测4小时的未来趋势,高精确度来自Forex数据集,这对于协助外汇模式决策至关重要。我们进一步评估了各种损失功能的绩效效果、衡量模型的模型和各种功能。