A Forecast Combination Framework with Multi-Time Scale for Livestock Products' Price Forecasting

Liwen Ling, Dabin Zhang, Amin W. Mugera, Shanying Chen, Qiang Xia

Research output: Contribution to journalArticle

Abstract

China's livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on the well-established idea of "forecast combination," a forecast combination framework with different time scales is proposed to improve the forecast accuracy for livestock products. Specifically, we combine the forecasts from multi-time scale, i.e., the short-term forecast and the long-term forecast. Forecasts derived from multi-time scale introduce complementary information about the dynamics of price movements, thus increasing the diversities within the modeling process. Moreover, we investigate a total of ten combination methods with different weighting schemes, including linear and nonlinear combination. The empirical results show that (i) forecast performance can be remarkably improved with this novel combination idea, and short-term forecast model is more suitable for the products with a relatively high volatility, e.g., mutton and beef; (ii) geometric mean, which provides a nonlinear combination, is the most effective one among all the combination methods; and (iii) variance-based weighting scheme can yield a superior result compared to the best individual forecast, especially for the products such as egg and beef.

Original languageEnglish
Article number8096206
JournalMathematical Problems in Engineering
Volume2019
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Forecast Combination
Beef
Agriculture
Forecast
Forecasting
Time Scales
Weighting
Framework
Geometric mean
Process Modeling
Volatility
China
Fluctuations

Cite this

@article{ce44b4354d3747b6a2ec23825a2241aa,
title = "A Forecast Combination Framework with Multi-Time Scale for Livestock Products' Price Forecasting",
abstract = "China's livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on the well-established idea of {"}forecast combination,{"} a forecast combination framework with different time scales is proposed to improve the forecast accuracy for livestock products. Specifically, we combine the forecasts from multi-time scale, i.e., the short-term forecast and the long-term forecast. Forecasts derived from multi-time scale introduce complementary information about the dynamics of price movements, thus increasing the diversities within the modeling process. Moreover, we investigate a total of ten combination methods with different weighting schemes, including linear and nonlinear combination. The empirical results show that (i) forecast performance can be remarkably improved with this novel combination idea, and short-term forecast model is more suitable for the products with a relatively high volatility, e.g., mutton and beef; (ii) geometric mean, which provides a nonlinear combination, is the most effective one among all the combination methods; and (iii) variance-based weighting scheme can yield a superior result compared to the best individual forecast, especially for the products such as egg and beef.",
author = "Liwen Ling and Dabin Zhang and Mugera, {Amin W.} and Shanying Chen and Qiang Xia",
year = "2019",
month = "1",
day = "1",
doi = "10.1155/2019/8096206",
language = "English",
volume = "2019",
journal = "Mathematical Problems in Engineering",
issn = "1024-123X",
publisher = "Hindawi Publishing Corporation",

}

A Forecast Combination Framework with Multi-Time Scale for Livestock Products' Price Forecasting. / Ling, Liwen; Zhang, Dabin; Mugera, Amin W.; Chen, Shanying; Xia, Qiang.

In: Mathematical Problems in Engineering, Vol. 2019, 8096206, 01.01.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A Forecast Combination Framework with Multi-Time Scale for Livestock Products' Price Forecasting

AU - Ling, Liwen

AU - Zhang, Dabin

AU - Mugera, Amin W.

AU - Chen, Shanying

AU - Xia, Qiang

PY - 2019/1/1

Y1 - 2019/1/1

N2 - China's livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on the well-established idea of "forecast combination," a forecast combination framework with different time scales is proposed to improve the forecast accuracy for livestock products. Specifically, we combine the forecasts from multi-time scale, i.e., the short-term forecast and the long-term forecast. Forecasts derived from multi-time scale introduce complementary information about the dynamics of price movements, thus increasing the diversities within the modeling process. Moreover, we investigate a total of ten combination methods with different weighting schemes, including linear and nonlinear combination. The empirical results show that (i) forecast performance can be remarkably improved with this novel combination idea, and short-term forecast model is more suitable for the products with a relatively high volatility, e.g., mutton and beef; (ii) geometric mean, which provides a nonlinear combination, is the most effective one among all the combination methods; and (iii) variance-based weighting scheme can yield a superior result compared to the best individual forecast, especially for the products such as egg and beef.

AB - China's livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on the well-established idea of "forecast combination," a forecast combination framework with different time scales is proposed to improve the forecast accuracy for livestock products. Specifically, we combine the forecasts from multi-time scale, i.e., the short-term forecast and the long-term forecast. Forecasts derived from multi-time scale introduce complementary information about the dynamics of price movements, thus increasing the diversities within the modeling process. Moreover, we investigate a total of ten combination methods with different weighting schemes, including linear and nonlinear combination. The empirical results show that (i) forecast performance can be remarkably improved with this novel combination idea, and short-term forecast model is more suitable for the products with a relatively high volatility, e.g., mutton and beef; (ii) geometric mean, which provides a nonlinear combination, is the most effective one among all the combination methods; and (iii) variance-based weighting scheme can yield a superior result compared to the best individual forecast, especially for the products such as egg and beef.

UR - http://www.scopus.com/inward/record.url?scp=85074982870&partnerID=8YFLogxK

U2 - 10.1155/2019/8096206

DO - 10.1155/2019/8096206

M3 - Article

VL - 2019

JO - Mathematical Problems in Engineering

JF - Mathematical Problems in Engineering

SN - 1024-123X

M1 - 8096206

ER -