智能生产力转型

Bojing Liu, Mengxiang Li, Zihui Ji, Hongming Li, Ji Luo
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引用次数: 0

摘要

随着深度学习技术向预测和决策支持系统的渗透,企业对准确预测时间序列数据的需求日益迫切。特别是在金融、零售和生产等领域,即时准确地预测市场趋势是保持竞争优势的关键。本研究旨在通过创新的深度学习框架,解决传统时间序列预测方法的局限性,如难以适应数据的非线性和非平稳性。作者提出了一种将深度学习与 LSTNet 和统计学相结合的先知模型。通过这种方式,他们将 LSTNet 处理复杂时间依赖性的能力与 Prophet 模型处理趋势和周期性的灵活性结合起来。粒子群优化算法(PSO)负责调整这一混合模型,旨在提高预测的准确性。这种策略不仅有助于捕捉时间序列中的长期依赖关系,还能很好地模拟季节性和假日效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Productivity Transformation
With the penetration of deep learning technology into forecasting and decision support systems, enterprises have an increasingly urgent need for accurate forecasting of time series data. Especially in fields such as finance, retail, and production, immediate and accurate predictions of market trends are the key to maintaining a competitive advantage. This study aims to address the limitations of traditional time series forecasting methods, such as the difficulty in adapting to the nonlinearity and non-stationarity of the data, through an innovative deep learning framework. The authors propose a Prophet model that combines deep learning with LSTNet and statistics. In this way, they combine the ability of LSTNet to handle complex time dependencies and the flexibility of the Prophet model to handle trends and periodicity. The particle swarm optimization algorithm (PSO) is responsible for tuning this hybrid model, aiming to improve the accuracy of predictions. Such a strategy not only helps capture long-term dependencies in time series, but also models seasonality and holiday effects well.
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