Recursive and direct multi-step forecasting: the best of both worlds
Recursive and direct multi-step forecasting: the best of both worlds
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发表时间：
2012
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通讯作者：
S. B. Taieb;Rob J Hyndman
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作者：
S. B. Taieb;Rob J Hyndman
我们提出了一种新的预测策略，称为纠正，旨在联合收割机的递归和直接预测策略的最佳性能相结合。校正策略背后的基本原理是开始时使用有偏差的递归预测，然后对其进行调整，使其无偏差且误差较小。我们使用线性和非线性模拟时间序列来研究纠正策略的性能，并将结果与递归策略和直接策略的结果进行比较。我们还进行了一些实验，使用真实的世界时间序列的M3和NN5预测比赛。我们发现，纠正策略总是优于，或至少有相当的性能，最好的递归和直接的策略。这一发现使得纠正策略非常有吸引力，因为它避免了在递归和直接策略之间做出选择，这在现实世界的应用中可能是一项困难的任务。
We propose a new forecasting strategy, called rectify, that seeks to combine the best properties of both the recursive and direct forecasting strategies. The rationale behind the rectify strategy is to begin with biased recursive forecasts and adjust them so they are unbiased and have smaller error. We use linear and nonlinear simulated time series to investigate the performance of the rectify strategy and compare the results with those from the recursive and the direct strategies. We also carry out some experiments using real world time series from the M3 and the NN5 forecasting competitions. We find that the rectify strategy is always better than, or at least has comparable performance to, the best of the recursive and the direct strategies. This finding makes the rectify strategy very attractive as it avoids making a choice between the recursive and the direct strategies which can be a difficult task in real-world applications.