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

机译：时间序列预测技术相对于噪声耐受性的比较

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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：时间序列的产生中普遍存在噪声。我们不能假设我们的源数据是干净的，数据总是（大部分时间）被噪声污染。噪声可能来自不同的来源：测量设备，传输装置等。本文对噪声的存在如何影响不同的预测技术进行了分析和比较。由于混沌时间序列最难预测，因此我们的研究基于这种时间序列。此外，存在一些表现出混沌行为的小型数学模型。通过随着时间的推移集成这些模型，我们可以生成干净的数据。然后，我们在不同的信噪比水平上添加噪声，并测量由不同预测技术生成的模型的性能。此比较中包括的预测技术是最近邻，人工神经网络，ARIMA，模糊神经网络和结合了差分进化的最近邻。在所有这些方法中，性能更好且受噪声影响较小的技术是“最近邻居”与“差分进化”相结合。

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