Forecasting time-series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with on-line inputs selection
Forecasting time-series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with on-line inputs selection
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DOI：
10.1109/fuzzy.2010.5584130
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发表时间：
2010-07
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通讯作者：
J. Andreu;P. Angelov
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作者：
J. Andreu;P. Angelov
在本文中，我们提出了用于从NN GC 1提供的关于运输数据集[1]的一些时间序列中预测14天范围的结果和算法。我们的方法是基于应用众所周知的进化Takagi-Sugeno（eTS）模糊系统[2-6]从时间序列中进行自学习。ETS的特点是，他们自我学习和发展的模糊规则为基础的系统，事实上，代表他们的结构，从数据流在线和实时模式。这意味着我们只使用了一次时间序列中的所有数据样本，在任何时刻，我们只使用了一个输入向量（由下面描述的几个数据样本组成），并且我们不存储或记忆整个序列。应该强调的是，这是一个巨大的实际优势，不幸的是，如果仅将精度/误差作为标准，则无法直接与NN GC 1中的其他竞争对手进行比较。同样值得的是，需要时间来提供计算和存储器使用以及迭代和计算复杂性，并进行比较，以构建所提出的技术提供的优势的更全面的画面。然而，我们提供了一种计算量小且易于使用的方法，该方法不需要指定任何特定于用户或问题的阈值或参数。此外，该方法不仅在其结构（基于模糊规则和自动自开发）方面是灵活的，而且在自动输入选择方面也是灵活的，如下面将描述的。
In this paper we present results and algorithm used to predict 14 days horizon from a number of time series provided by the NN GC1 concerning transportation datasets [1]. Our approach is based on applying the well known Evolving Takagi-Sugeno (eTS) Fuzzy Systems [2–6] to self-learn from the time series. ETS are characterized by the fact that they self-learn and evolve the fuzzy rule-based system which, in fact, represents their structure from the data stream on-line and in real-time mode. That means we used all the data samples from the time series only once, at any instant in time we only used one single input vector (which consist of few data samples as described below) and we do not iterate or memorize the whole sequence. It should be emphasized that this is a huge practical advantage which, unfortunately cannot be compared directly to the other competitors in NN GC1 if only precision/error is taken as a criteria. It is also worth to require time for calculations and memory usage as well as iterations and computational complexity to be provided and compared to build a fuller picture of the advantages the proposed technique offers. Nevertheless, we offer a computationally light and easy to use approach which in addition does not require any user-or problem-specific thresholds or parameters to be specified. Additionally, this approach is flexible in terms not only of its structure (fuzzy rule based and automatic self-development), but also in terms of automatic input selection as will be described below.