کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
997594 | 1481456 | 2011 | 17 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting](/preview/png/997594.png)
This paper presents a data-driven approach applied to the long term prediction of daily time series in the Neural Forecasting Competition. The proposal comprises the use of adaptive fuzzy rule-based systems in a top-down modeling framework. Therefore, daily samples are aggregated to build weekly time series, and consequently, model optimization is performed in a top-down framework, thus reducing the forecast horizon from 56 to 8 steps ahead. Two different disaggregation procedures are evaluated: the historical and daily top-down approaches. Data pre-processing and input selection are carried out prior to the model adjustment. The prediction results are validated using multiple time series, as well as rolling origin evaluations with model re-calibration, and the results are compared with those obtained using daily models, allowing us to analyze the effectiveness of the top-down approach for longer forecast horizons.
Journal: International Journal of Forecasting - Volume 27, Issue 3, July–September 2011, Pages 708–724