第二章运用人工智能技术进行经济时间序列预测

Utku Kose
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引用次数: 1

摘要

在本章中,作者将重点介绍使用机器学习技术来预测未来经济状态的数据,这些技术包括人工神经网络、自适应神经模糊推理系统、动态玻尔兹曼机、支持向量机、隐马尔可夫模型、高斯过程模型上的贝叶斯学习、自回归综合移动平均、自回归模型(Poggi, Muselli, Notton, Cristofari, & Louche, 2003)和k -近邻算法。研究结果显示,在预测经济数据方面取得了积极成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chapter 2 Using Artificial Intelligence Techniques for Economic Time Series Prediction
In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.
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