Exploring the Effect of Sequence Smoothness on Machine Learning Accuracy

Time-series data, amassed through regular intervals over time, has become abundant with the rapid evolution of science and technology. Analyzing these time series unveils inherent dynamics, facilitating a nuanced understanding of diverse social phenomena. Moreover, such analyses empower future predictions, establishing a scientific foundation for informed decision-making and regulatory measures. Consequently, the importance of enhancing the accuracy of time-series predictions cannot be overstated. Addressing the challenges posed by large-scale, nonlinear, and non-smooth time-series data (Livieris et al., 2021), numerous machine learning-based time-series prediction methods have emerged and found extensive application across various industries. Long Short-Term Memory (LSTM) networks (Hu et al. 57, 2022), characterized by their specialized architecture facilitating long-term memory retention, are particularly well-suited for modeling and predicting real-world time-series data (Wang et al, 2024). While significant research efforts have concentrated on optimizing the LSTM model structure to enhance prediction accuracy, limited attention has been given to the preprocessing inadequacies in machine learning for time-series data. This paper adopts a perspective grounded in statistical theory and machine learning methods to explore the impact of sequence smoothness on machine learning accuracy through data preprocessing. Utilizing Python software, the study simulates eight non-smooth series, including random wandering with smooth + AR(1) sequence, random wandering with + MA(2) sequence, random wandering with + smooth ARMA(1, 1) sequence, random wandering with + smooth SARMA(1, 10, 1) sequence, linear trending smooth sequence, linear trending with + smooth AR(2) sequence, quadratic trending smooth sequence, and linear trending with + smooth AR(1, 10, 1) sequence, each of length 300. Subsequently, employing the time-series split cross-validation method, the original data and differenced data are segmented into multiple training and testing sets for model training and prediction. The study calculates the prediction accuracy of the original series and the inverse prediction accuracy of the differenced series. Comparisons of LSTM learning effects before and after differential smoothing reveal that preprocessing the original series with differential smoothing significantly improves the learning accuracy of LSTM when modeling non-stationary time series (Dixit et al. 2021). Lastly, to validate the generality of the findings, daily closing price data of SSE and SZSE indices from December 31, 2012, to December 31, 2022, are utilized. To optimize the original LSTM from a data preprocessing perspective, a novel differential-LSTM algorithm, combining differential smoothing with LSTM, is proposed. This approach contributes to advancing the understanding and application of machine learning in optimizing time-series prediction accuracy.