A Program for the Improvement and Elaboration of Data Needed for Commodity Price Forecasting

1930 ◽  
Vol 12 (1) ◽  
pp. 107
Author(s):  
O. C. Stine
Keyword(s):  
Commodity Price ◽  
Price Forecasting

Commodity price forecasting

2021 ◽  
pp. 78-96
Author(s):  
Mikidadu Mohammed
Keyword(s):  
Commodity Price ◽  
Price Forecasting

scholarly journals A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Zhang ◽  
Sanggyun Na
Keyword(s):  
Price Index ◽  
Commodity Price ◽  
Series Data ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Fuzzy Information ◽  
Agricultural Commodity ◽  
Information Granulation ◽  
Svm Model ◽  
Fuzzy Information Granulation

Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.

Economic evaluation of commodity price forecasting models

1993 ◽  
Vol 9 (3) ◽  
pp. 387-397 ◽  
Author(s):  
Mary E. Gerlow ◽  
Scott H. Irwin ◽  
Te-Ru Liu
Keyword(s):  
Economic Evaluation ◽  
Commodity Price ◽  
Price Forecasting ◽  
Forecasting Models

scholarly journals Gold Price Forecasting using Box-Jenkins Method

2019 ◽  
Vol 1 (3) ◽  
pp. 15-27
Author(s):  
Nur Atikah Khalid ◽  
Nurfadhlina Abdul Halim
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Time Series Analysis ◽  
Commodity Prices ◽  
Commodity Price ◽  
Percentage Error ◽  
Price Forecasting ◽  
Analysis Method ◽  
Current Trends ◽  
Future Value

In general, the nature of gold that acts as a hedge against inflation and its stable price over the course of the financial crisis has made it a unique commodity. Price forecasts are a must for gold producers, investors and central bank to know the current trends in gold prices. Forecasting the future value of a variable is often done with time series analysis method. This study was conducted to determine the best model for forecasting gold commodity prices as well as forecasting world gold commodity prices in 2018 using Box-Jenkins approach. The data used in this study was obtained from Investing.com from 2015 until 2017. This study shows that ARIMA (1,1,1) is the best model to predict gold commodity prices based on Mean Absolute Percentage Error (MAPE). MAPE value for ARIMA (1,1,1) is 0.02%, where this value proves that forecasting using ARIMA (1,1,1) is the best forecasting because MAPE value is less than 10%.

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