English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. Th is study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifi cally, the EEMD is fi rst used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. Th e fi ne-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fl uctuation, the low-frequency fl uctuation, and the trend terms which can highlight new features of the hog price fl uctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fl uctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fi t the high-frequency fl uctuation and trend term, respectively, in a multistep-ahead fashion. Th e commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. Th e monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. Th e numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting.

An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China