Time series have become a valuable source of study in many areas, mainly because it encapsulates some underlying time-index variables. A significant part of these studies is dedicated to fit a single model to the past data to forecast future values of the series. However, single models may not be able to adequately fit local patterns; that is, particular and eventually recurrent variations dynamically incorporated in the series as time evolves. This temporal-window oriented paradigm has been at the vanguard of time series modeling and forecasting exercises. The present paper proposes a simple local-pattern oriented system to model and forecast time series. Our approach involves three steps: (i) the time series is split into $k$ subsets in such a way that each subset may intercept its neighbors; (ii) each subset is modelled, considering lags according to confidence intervals of the auto-correlation function; and (iii) pattern recognition of the target values of the time series in relation to the modeled subsets, via dynamic time warping. The usefulness of the proposed framework is illustrated by modeling and forecasting real-world time series. Evaluation metrics were adopted to compare the proposed approach with multilayer perceptron neural networks and support vector regression predictors. The results provided by published models are also taken into account and it was found that the proposed system presented better performance than the compared models in the experiments.

时间序列已成为许多领域的重要研究资源，主要是因为它封装了一些潜在的时间索引变量。这些研究的很大一部分致力于使单个模型适合过去的数据，以预测该系列的未来价值。但是，单个模型可能无法充分适应局部模式；也就是说，随着时间的流逝，特定的，最终的周期性变化会动态地纳入系列中。这种面向时间窗口的范例一直是时间序列建模和预测练习的先锋。本文提出了一个简单的面向本地模式的系统来对时间序列进行建模和预测。我们的方法涉及三个步骤：（i）时间序列分为$ķ$子集的方式使得每个子集可以拦截其邻居；（ii）对每个子集进行建模，并根据自相关函数的置信区间考虑滞后；（iii）通过动态时间扭曲对与建模子集相关的时间序列目标值进行模式识别。通过建模和预测现实世界时间序列来说明所提出框架的有用性。采用了评估指标，以将所提出的方法与多层感知器神经网络和支持向量回归预测器进行比较。还考虑了已发布模型提供的结果，并且发现在实验中，所提出的系统表现出比比较模型更好的性能。