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- W2557274016 abstract "SYNOPTIC ABSTRACTIn this article we explore the hierarchical nature of time series of various agriculture crops in Pakistan at the subdistrict level and produce short-term forecasts for these time series. The data and forecasts are organized in a hierarchy based on disaggregating the data according to the division and subdistrict levels (geographical regions). Following Athanasopoulos, Ahmed, and Hyndman, we consider five approaches to hierarchical forecasting; two variations of the top-down approach; the bottom-up method; top-down approach where top-level forecasts are disaggregated according to the forecasted proportions of the lower level series; and the new optimal combination approach introduced by Hyndman, Ahmed, Athanasopoulos, and Shang. The forecasts are obtained from these five approaches using two well-known methods; Exponential Smoothing (ES) and an autoregressive integrated moving average (ARIMA). The forecasts are then compared across the two methods and across the various approaches by usin..." @default.
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- W2557274016 date "2016-11-30" @default.
- W2557274016 modified "2023-09-22" @default.
- W2557274016 title "Hierarchical Forecasts of Agronomy-Based Data" @default.
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- W2557274016 doi "https://doi.org/10.1080/01966324.2016.1246990" @default.
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