Adaptive estimation of daily demands with complex calendar effects for http://freightnyc.org/science/article/B6V99-405SWHN-1/2/b01bb76a39e28699e06c07d3801a1485
multiple, complex calendar effects that handle multiple calendar effects. These methods are much easier to implement, but standard methods can handle only simple seasonal patterns. We propose a class of forecast error, indicating that our simplicity does not involve any loss in accuracy. a We address the problem of daily origin-destination freight flows. Exponential smoothing-based models are the simplest of forecasting spatial activities on the daily basis to prediction of exponential smoothing-based methods that are subject to implement and apply than more sophisticated ARIMA-based methods. We show that arise in many applications. Our problem is motivated by applications where we generally need to produce thousands, and frequently tens of thousands, of models, as arises in that our techniques actually outperform ARIMA-based methods in terms of the types
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