کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6894815 1445932 2018 32 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Exploring the sources of uncertainty: Why does bagging for time series forecasting work?
ترجمه فارسی عنوان
بررسی منابع عدم اطمینان: چرا جعبه سازی برای پیش بینی های سری زمانی کار می کند؟
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی
In a recent study, Bergmeir, Hyndman and Benítez (2016) successfully employed a bootstrap aggregation (bagging) technique for improving the performance of exponential smoothing. Each series is Box-Cox transformed, and decomposed by Seasonal and Trend decomposition using Loess (STL); then bootstrapping is applied on the remainder series before the trend and seasonality are added back, and the transformation reversed to create bootstrapped versions of the series. Subsequently, they apply automatic exponential smoothing on the original series and the bootstrapped versions of the series, with the final forecast being the equal-weight combination across all forecasts. In this study we attempt to address the question: why does bagging for time series forecasting work? We assume three sources of uncertainty (model uncertainty, data uncertainty, and parameter uncertainty) and we separately explore the benefits of bagging for time series forecasting for each one of them. Our analysis considers 4004 time series (from the M- and M3-competitions) and two families of models. The results show that the benefits of bagging predominantly originate from the model uncertainty: the fact that different models might be selected as optimal for the bootstrapped series. As such, a suitable weighted combination of the most suitable models should be preferred to selecting a single model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 268, Issue 2, 16 July 2018, Pages 545-554
نویسندگان
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