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Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system

机译：基于线性模糊信息颗粒和模糊推理系统的时间序列长期预测

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Long-term time series forecasting is a challenging problem both in theory and in practice. Although the idea of information granulation has been shown to be an essential concept and algorithmic pursuit in time series prediction, there is still an acute need for developing a sound conceptual framework for time series prediction so that information granulation can capture the essence of collections of data better, including average and trend information. In this paper, a novel type of fuzzy information granule involving a time dependent (non-stationary) membership function is proposed to structure numerical time series into granular time series. We show that the underlying arithmetic along with the concept of distance for this type of information granules can be expressed in a simple way, which facilitates the ensuing processing of information granules. With this regard, distances between observation granules and antecedent granules presented in fuzzy rules can be easily determined. The design of long-term prediction method based on fuzzy inference system is then realized through interpolation completed with the aid of fuzzy rules. Experiments involving chaotic Mackey-Glass time series and real-world time series demonstrate that the proposed model produces better long-term forecasting than some existing numeric models such as Autoregressive (AR) models, nonlinear autoregressive (NAR) neural networks, Support Vector Regression (SVR) and fuzzy inference systems involving triangular and interval information granules. (C) 2016 Elsevier Inc. All rights reserved.
机译：长期时间序列预测在理论上和实践上都是一个具有挑战性的问题。尽管信息粒化的概念已被证明是时间序列预测中必不可少的概念和算法追求，但仍然迫切需要开发合理的时间序列预测概念框架，以便信息粒化可以捕获数据集合的本质更好，包括平均值和趋势信息。本文提出了一种新型的包含时间相关（非平稳）隶属函数的模糊信息颗粒，将数值时间序列构造为颗粒时间序列。我们表明，可以用一种简单的方式来表达这种类型的信息颗粒的基本算法以及距离的概念，这有助于随后对信息颗粒的处理。鉴于此，可以容易地确定以模糊规则表示的观察颗粒和先行颗粒之间的距离。通过模糊规则的插值，实现了基于模糊推理系统的长期预测方法的设计。涉及混沌Mackey-Glass时间序列和现实世界时间序列的实验表明，与某些现有数值模型（例如自回归（AR）模型，非线性自回归（NAR）神经网络，支持向量回归（ SVR）和涉及三角形和区间信息颗粒的模糊推理系统。 （C）2016 Elsevier Inc.保留所有权利。

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