一种预测印度食品批发价格指数的极限学习机方法

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pertanika Journal of Science and Technology Pub Date : 2023-10-12 DOI:10.47836/pjst.31.6.30
Dipankar Das, Satyajit Chakrabarti
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引用次数: 0

摘要

精确的粮食价格预测对任何国家都是至关重要的,从各种可用的战略中寻找实现这一目标的适当方法是一个悬而未决的问题。目前的印度批发价格指数(WPI)系列包含60个单独的食品项目在“食品制造”类别。本文考虑的是2011年4月至2022年6月的月度数据,即这60个wpi的135个月的数据。研究人员提取了每个WPI的线性、曲率和自相关特征。这些wpi的曲率和线性分组揭示了wpi的异质性。本工作提出了一种极限学习机(ELM)方法来预测这些wpi。目前的工作采用了以下22种时间序列预测技术:6种标准方法(Auto ARIMA、TSLM、SES、DES、TES和Auto ETS), 5种神经网络(Auto FFNN、Auto GRNN、Auto MLP、Auto ELM和提议的ELM),以及11种最先进的技术(2种基于ARIMA-ETS的集成、1种基于ARIMA- thetaf - tbats的集成、1种MLP和7种基于lstm的模型),以确定这些wpi的最佳预测方法。对于大多数wpi,所提供的ELM在15个月的样本外预测中达到了合适的性能。近87%的病例达到了高准确度(MAPE≤10),并超过了其他病例。通过对预测- mape和预测- rmse的精度比较,本文观察到所提出的ELM与其他方法的性能更有利。本文的研究结果表明,所提出的ELM在提供这60个wpi的准确预测方面具有很好的前景。
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An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India
Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE ≤ ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs.
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来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
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