Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

Ahmar, Ansari Saleh; Singh, Pawan Kumar; Ruliana, R.; Pandey, Alok Kumar; Gupta, Stuti

doi:10.3390/forecast5010006

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…For greater forecast accuracy, two or more models are combined (hybrid). When data redundancy is noticed or for best model accuracy with small error, the hybrid model is used [ 20 ]. Based on our research objective, it is necessary to construct a hybrid time-series model for estimating pulses production behaviour to fulfil the demand–supply gap, which is affected by food security.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Pulses Production in India: Retrospect and Prospects of the Future Food with an Application of Hybrid Models

Mishra

Khatib

Lal

et al. 2023

Natl. Acad. Sci. Lett.

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Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of protein needed for human health. India's pulses production guidelines must be based on accurate and best forecast models. Comparing classical statistical and machine learning models based on different scientific data series is the subject of high-level research today. This study focused on the forecasting behaviour of pulses production for India, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh. The data series was split into a training dataset (1950–2014) and a testing dataset (2015–2019) for model building and validation purposes, respectively. ARIMA, NNAR and hybrid models were used and compared on training and validation datasets based on goodness of fit (RMSE, MAE and MASE). This research demonstrates that due to the diverse agricultural conditions across different provinces in India, there is no single model that can accurately predict pulse production in all regions. This study’s highest accuracy model is ARIMA. ARIMA outperforms NNAR, a machine learning model. Pulse production in India, Rajasthan, and Madhya Pradesh will expand by 26.11%, 12.62%, and 0.51% from 2020 to 2030, whereas it would decline by − 6.5%, − 6.21%, and − 6.76 per cent in Karnataka, Maharashtra, and Uttar Pradesh, respectively. The current forecast results could allow policymakers to develop more aggressive food security and sustainability plans and better Indian pulses production policies in the future.

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Section: Discussionmentioning

confidence: 99%

“…A training dataset (1950–2014) and a testing dataset are used for model construction and validation (2015–2019). The best model in this study was ARIMA model over NNAR model because of its superior out-of-sample predictions [ 17 , 20 ]. High-level research compares traditional statistics and machine learning approaches.…”

Section: Discussionmentioning

confidence: 99%

An Overview of Pulses Production in India: Retrospect and Prospects of the Future Food with an Application of Hybrid Models

Mishra

Khatib

Lal

et al. 2023

Natl. Acad. Sci. Lett.

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“…A widely used method is time series analysis, which involves techniques for analyzing time series data to extract meaningful statistical attributes and characteristics of the data. The initial approach is decomposing the series, and commonly used methods are the Holt-Winters method 7 or the Census II X-11 method. 8 The autoregressive integrated moving average (ARIMA) approach is a widely used statistical method for analyzing and forecasting time series data.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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“…The Holt-Winters method has been used individually or together with other methods in fields strictly linked to sustainable development or related to it, such as health [17,18], energy [19,20], pollution [21] and agriculture [22], but not limited to these [23,24].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Sustainable Development Indicators in Romania: A Study in the European Context

Ciucu (Durnoi),

Ioanăș,

Iordan

et al. 2024

Sustainability

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Sustainable development is a very important and highly debated concept worldwide, with almost all states around the globe recognizing the importance of supporting a balancing of economic, social and ecological plans to create a more prosperous and equitable world. The purpose of the current study is to identify the current context of the progress achieved by the EU member states regarding the 17 sustainable development objectives. Following the implemented research, it was found that Romania is one of the countries with the lowest performance, so an attempt was made to make a short-term forecast in the case of this country in order to determine its future course. In this sense, the Holt–Winters trend methods were used, which are based on historical data to predict future values. According to the analysis carried out, the predicted values are mainly encouraging, but it is necessary for the competent authorities to become more involved because, in many aspects, Romania is at the bottom of the European Union (EU) ranking regarding the analyzed indicators.

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Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

Cited by 6 publications

References 44 publications

An Overview of Pulses Production in India: Retrospect and Prospects of the Future Food with an Application of Hybrid Models

An Overview of Pulses Production in India: Retrospect and Prospects of the Future Food with an Application of Hybrid Models

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Forecasting Sustainable Development Indicators in Romania: A Study in the European Context

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