Chapter 2 Using Artificial Intelligence Techniques for Economic Time Series Prediction

Utku Kose
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引用次数: 1

Abstract

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.
第二章运用人工智能技术进行经济时间序列预测
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。
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CiteScore
1.40
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