Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

Abstract

The fluctuations of agricultural commodity prices have a great impact on people's daily lives as well as the inputs and outputs of agricultural production. An accurate forecast of commodity prices is therefore essential if agricultural authorities are to make scientific decisions. To forecast prices more adaptively, this study proposes a novel model selection framework which includes time series features and forecast horizons. Twenty-nine features are used to depict agricultural commodity prices and three intelligent models are specified as the candidate forecast models; namely, artificial neural network (ANN), support vector regression (SVR), and extreme learning machine (ELM). Both random forest (RF) and support vector machine (SVM) are applied to learn the underlying relationships between the features and the performances of the candidate models. Additionally, a minimum redundancy and maximum relevance approach (MRMR) is employed to reduce feature redundancy and further improve the forecast accuracy. The experimental results demonstrate that, firstly, the proposed model selection framework has a better forecast performance compared with the optimal candidate model and simple model average; secondly, feature reduction is a workable approach to further improve the performance of the model selection framework; and thirdly, for bean and pig grain products, different distributions of the time series features lead to a different selection of the optimal models. INDEX TERMS Model selection, agricultural commodity, price forecasting, time series features, forecast horizons.

Wheat Futures Prices Prediction in China: A Hybrid Approach

“…By suggestion, this would address the issue of whether representatives, dealers, and examiners in the China wheat market can build up a beneficial exchanging technique [16] and whether that would help in supporting against changes in spot costs [8,22]. e authors in [23] developed a novel model selection framework based on time series features and forecast horizons. A number of features were used to illustrate agricultural commodity prices, and three hybrid models were identified as the best forecast models, namely, support vector regression (SVR), artificial neural network (ANN), and extreme learning machine (ELM).…”

“…A number of features were used to illustrate agricultural commodity prices, and three hybrid models were identified as the best forecast models, namely, support vector regression (SVR), artificial neural network (ANN), and extreme learning machine (ELM). Furthermore, the authors in [23] applied random forest (RF) and support vector machine (SVM) to study the primary relationships between the features and the performances of the candidate models. In these investigations, various examinations have analyzed the consistency of the rural items prospects costs fundamentally, and every one of these investigations has focused on the US and hardly any other futures markets.…”

Multivariate time series prediction of high dimensional data based on deep reinforcement learning

“…High dimensional data is a feedforward learning data processing model, and its structure is very similar to radial basis function. Compared with the RBF data processing model, the generalized high-dimensional data has more advantages in approximation ability and convergence speed [5][6] . The multivariable time series pre prediction method uses the correlation between multiple time series to improve the overall prediction accuracy.…”

Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis

“…Price analysis and prediction for agricultural commodities is a key element for decisionand policy-making [1], e.g., farmers require price analysis to accurately trade their stock [2], and governmental institutions rely on accurate price estimations to guarantee the favorable operation of the economy [3]. Therefore, the estimation of prices for the construction of different scenarios must be a corporate strategy when making short-and long-term plans [4], and when creating policies to promote appropriate programs for the development of the agricultural economy, food security, and coverage costs [5].…”

“…Accurate price analyses and forecasting is a long-pursued objective [10]. The latest developments in information technologies have created interesting approaches for the analysis and prediction of agricultural prices [11], including machine and deep learning models [3], multiple linear regression analyses [12], the vector error correction model and multi-output support vector regression [5], autoregressive integrated moving averages, partial least squares and artificial neural networks [13], and autoregressive integrated moving averages and Elman neural networks [14], among others. Some disadvantages of the traditional statistical techniques for price estimation include, e.g., the need for historical information and the inability to consider the expectations and knowledge of experts and decision-makers [15].…”