State of the art in total pulse production in major states of India using ARIMA techniques

Mishra, Pradeep; Yonar, Aynur; Yonar, Harun; Kumari, Binita; Abotaleb, Mostafa; Das, Sudipta; Patil, S. R.

doi:10.1016/j.crfs.2021.10.009

Cited by 29 publications

(18 citation statements)

References 10 publications

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“…An investigation on the effect of post-traumatic stress disorder (PTSD) on various factors including heart rate by [ 18 ] employed ARIMA models to analyze the heart rate data. An ARIMA model was used to capture the trend of pulse production in India by [ 19 ] to predict pulse production from 2020 to 2029 to bridge the gap between the supply and demand. Two time series models were employed to estimate the growth rate of glioblastoma in response to ionizing radiotherapy treatment in a comparative study presented by [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Predictive Analysis of Heart Rates Using Machine Learning Techniques

Oyeleye

Chen

Titarenko

et al. 2022

IJERPH

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Heart disease, caused by low heart rate, is one of the most significant causes of mortality in the world today. Therefore, it is critical to monitor heart health by identifying the deviation in the heart rate very early, which makes it easier to detect and manage the heart’s function irregularities at a very early stage. The fast-growing use of advanced technology such as the Internet of Things (IoT), wearable monitoring systems and artificial intelligence (AI) in the healthcare systems has continued to play a vital role in the analysis of huge amounts of health-based data for early and accurate disease detection and diagnosis for personalized treatment and prognosis evaluation. It is then important to analyze the effectiveness of using data analytics and machine learning to monitor and predict heart rates using wearable device (accelerometer)-generated data. Hence, in this study, we explored a number of powerful data-driven models including the autoregressive integrated moving average (ARIMA) model, linear regression, support vector regression (SVR), k-nearest neighbor (KNN) regressor, decision tree regressor, random forest regressor and long short-term memory (LSTM) recurrent neural network algorithm for the analysis of accelerometer data to make future HR predictions from the accelerometer’s univariant HR time-series data from healthy people. The performances of the models were evaluated under different durations. Evaluated on a very recently created data set, our experimental results demonstrate the effectiveness of using an ARIMA model with a walk-forward validation and linear regression for predicting heart rate under all durations and other models for durations longer than 1 min. The results of this study show that employing these data analytics techniques can be used to predict future HR more accurately using accelerometers.

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Section: Introductionmentioning

confidence: 99%

A Predictive Analysis of Heart Rates Using Machine Learning Techniques

Oyeleye

Chen

Titarenko

et al. 2022

IJERPH

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“…The ARIMA model was used to anticipate wholesale paddy prices in 5 major Indian states for the coming crop year (19). Reports are on the ARIMA method to investigate the trend in total pulse production in India (20). In the special context of COVID-19, reports are on the variables that affect vegetable price variations in both vertical and horizontal dimensions an ARIMA model of short-term price production was used and its performance was assessed (21).…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling for Forecasting Fertilizer Consumption in India

Borkar

2023

Plant Sci. Today

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Fertilizers have contributed significantly to increased agricultural yields, particularly for cereal crops and they will still be an important part of the science-based farming that is needed to feed the world's growing population. Fertilizers replenish the soil nutrients lost by the harvested crops, promote the use of high-yielding cultivars and boost biomass in tropical soils that are deficient in nutrients. In this study, data on fertilizer consumption in India was gathered from Agricultural Statistics at a Glance from 1950-51 to 2020-21 and utilized to fit the ARIMA model and forecast future usage. Forecasting has been done using the Box-Jenkins ARIMA approach. The ARIMA model is the most popular and widely applied forecasting model for time series data. The data was calculated using autocorrelation and partial autocorrelation functions. R programming software was used to estimate model parameters. The performance of the fitted model was evaluated using various goodness of fit criteria, such as AIC, BIC and MAPE. Empirical results revealed that the ARIMA (1,2,1) model was best suited to forecasting India's future total fertilizer use. Similarly, the ARIMA model was fitted for nitrogen, phosphorus, and potassium consumption in India independently. Forecasts from 2021-22 to 2030-31 are calculated using the chosen model. By 2030-31, total fertilizer use is predicted to reach 32,058.55 thousand tonnes. Policymakers should preferably base their judgments on reliable forecasts in order to tighten policies and achieve outcomes. Predicting future events using an appropriate time series model will assist policymakers, marketing strategies in making decisions related to export/ import and developing appropriate fertilizer consumption strategies.

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“…MAPE and MASE are scale-free [ 16 ]. The MASE is the most adaptive and reliable forecast accuracy measure [ 17 ].The MAE and RMSE are expressed as follows: where denotes the actual values, represent the predicted values and indicates the number of observations.…”

Section: Methodsmentioning

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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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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State of the art in total pulse production in major states of India using ARIMA techniques

Cited by 29 publications

References 10 publications

A Predictive Analysis of Heart Rates Using Machine Learning Techniques

A Predictive Analysis of Heart Rates Using Machine Learning Techniques

Statistical Modeling for Forecasting Fertilizer Consumption in India

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

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