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 language | English |
|---|---|
| Article number | 8096206 |
| Journal | Mathematical Problems in Engineering |
| Volume | 2019 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
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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 journal › Article
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 -