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Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing

机译:使用简约季节指数平滑法预测具有多个季节周期的日内时间序列

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This paper concerns the forecasting of seasonal intraday time series that exhibit repeating intraweek and intraday cycles. A recently proposed exponential smoothing method involves smoothing a different intraday cycle for each distinct type of day of the week. Similar days are allocated identical intraday cycles. A limitation is that the method allows only whole days to be treated as identical. We introduce a new exponential smoothing formulation that allows parts of different days of the week to be treated as identical. The result is a method that involves the smoothing and initialisation of fewer terms. We evaluate forecasting up to a day ahead using two empirical studies. For electricity load data, the new method compares well with a range of alternatives. The second study involves a series of arrivals at a call centre that is open for a shorter duration at the weekends than on weekdays. Among the variety of methods considered, the new method is the only one that can model in a satisfactory way in this situation, where the number of periods on each day of the week is not the same.
机译:本文涉及季节性日间时间序列的预测,这些时间序列显示出周内和日内重复的周期。最近提出的指数平滑方法涉及针对一周中每种不同类型的一天平滑不同的盘中周期。相似的日期被分配相同的盘中周期。局限性在于该方法仅允许将整天视为相同。我们引入了一种新的指数平滑公式,该公式允许将一周中不同日期的不同部分视为相同。结果是涉及较少项的平滑和初始化的方法。我们使用两项经验研究来评估最多一天的预测。对于电力负荷数据,新方法可以与一系列替代方法进行比较。第二项研究涉及一系列到达呼叫中心的呼叫,该呼叫中心在周末的开放时间比工作日短。在考虑的各种方法中,新方法是唯一可以在这种情况下以令人满意的方式建模的方法,在这种情况下,一周中每一天的周期数不相同。

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