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Comparison of time series forecasting techniques with respect to tolerance to noise

机译：时间序列预测技术对噪声耐受性的比较

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摘要

Noise is ubiquitous in the production of time series; we cannot assume that our source data is clean, data is always (most of the time) contaminated with noise. Noise may come from different sources: measuring devices, transmission means, etc. This article presents an analysis and comparison of how the presence of noise affects different forecasting techniques. Since chaotic time series are the most difficult to predict, we base our study on this kind of time series. Furthermore, there exist several small mathematical models that exhibit chaotic behavior. We can produce clean data by integrating those models over time. We then add noise at different levels of Noise to Signal Ratios, and measure the performance of the models produced by different forecasting techniques. The forecasting techniques included in this comparison are Nearest Neighbors, Artificial Neural Networks, ARIMA, Fuzzy Neural Networks, and Nearest Neighbors combined with Differential Evolution. Among all of them, the technique that performs better and is less affected by noise is Nearest Neighbors combined with Differential Evolution.
机译：噪音在时间序列的生产中普遍存在;我们不能认为我们的源数据是干净的，数据始终（大多数时间）被污染的噪声污染。噪音可能来自不同的来源：测量装置，传输装置等。本文介绍了噪声存在如何影响不同预测技术的分析和比较。由于混乱的时间序列是最难预测的，因此我们基于对这种时间序列的研究。此外，存在几种表现出混沌行为的小型数学模型。我们可以通过随着时间的推移整合这些模型来生产清洁数据。然后，我们将噪声与信号比的不同噪声增加，并测量不同预测技术产生的模型的性能。在该比较中包括的预测技术是最近的邻居，人工神经网络，Arima，模糊神经网络和最近的邻居与差分演进相结合。在所有这些中，执行更好并且受到噪声影响的技术是最接近的邻居与差分演进相结合。

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