高级时间序列需求预测方法:分解与深度学习混合模型

IF 4.3
Juyoung Ha , Sungwon Lee , Sooyeon Jeong , Doohee Chung
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引用次数: 0

摘要

数据科学的进步越来越多地集中在改进时间序列预测模型,以有效地进行企业管理和需求预测。传统模型常常难以捕捉时间序列数据中的不规则模式。在这项研究中,我们采用了一种新的混合模型,集成了集成经验模式分解(EEMD)、最小绝对收缩和选择算子(LASSO)和长短期记忆(LSTM)网络来解决这些挑战。我们的方法遵循结构化的管道:EEMD将时间序列数据分解为集成的内在模式函数(eIMFs)以揭示复杂的模式,LASSO选择最相关的特征来优化输入变量,LSTM捕获长期依赖关系以进行准确的需求预测。我们根据来自三个行业(办公产品、包装材料和制药)的真实需求数据评估我们的模型,并使用NRMSE、NMAE和R2将其与ARIMAX、LightGBM、LSTM及其eemd增强变体进行比较。结果表明,将EEMD集成到基线模型中,平均降低了27.4%的NRMSE,而额外加入LASSO进一步提高了性能,实现了29.1%的总降低。与独立LSTM模型相比,我们提出的EEMD-LASSO-LSTM模型的NRMSE大幅降低了51.2%,突出了其优越的预测精度。这种EEMD、LASSO和LSTM的创新组合使我们提出的方法能够有效地捕捉需求的不规则模式,这是传统预测方法的一个重大障碍。EEMD、LASSO和LSTM的集成标志着时间序列预测建模的重大进步,增强了需求预测和为企业战略决策提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methodology for advanced time series demand forecasting: A hybrid model of decomposition and deep learning
Advancements in data science have increasingly focused on refining time-series predictive models for effective corporate management and demand forecasting. Traditional models often struggle to capture irregular patterns in time-series data. In this study, we employ a novel hybrid model integrating Ensemble Empirical Mode Decomposition (EEMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Long Short-Term Memory (LSTM) networks to address these challenges. Our approach follows a structured pipeline: EEMD decomposes time-series data into ensemble Intrinsic Mode Functions (eIMFs) to reveal complex patterns, LASSO selects the most relevant features to optimize input variables, and LSTM captures long-term dependencies for accurate demand forecasting. We evaluate our model on real-world demand data from three industries (Office Product, Packaging Materials, and Pharmaceuticals), comparing it against ARIMAX, LightGBM, LSTM, and their EEMD-enhanced variants using NRMSE, NMAE, and R2. Results show that integrating EEMD into baseline models reduces NRMSE by an average of 27.4%, while the additional incorporation of LASSO further improves performance, achieving a total reduction of 29.1%. Compared to the standalone LSTM model, our proposed EEMD-LASSO-LSTM model demonstrates a substantial NRMSE reduction of 51.2%, highlighting its superior predictive accuracy. This innovative combination of EEMD, LASSO, and LSTM enables our proposed method to effectively capture the irregular patterns of demand, a task that has been a significant hurdle for conventional forecasting methods. The integration of EEMD, LASSO, and LSTM marks a significant advancement in time-series predictive modeling, enhancing demand forecasting and informing strategic corporate decisions.
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CiteScore
5.60
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0.00%
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