Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Network

Mahto, Anil Kumar; Alam, M. Afshar; Biswas, Ranjit; Ahmed, Jawed; Alam, Shah Imran

doi:10.1155/2021/9939906

Cited by 39 publications

(14 citation statements)

References 38 publications

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“…This limitation is typical of the ARIMA and ARIMA models with Kalman filter, as found in other studies [4,17,43,45]. Even more, other predictive systems based on a priori prediction show the same limitation as we can see in recent studies [46][47][48][49]. Therefore, we leave this as a limitation associated with the nature of our system that must be taken into account when using it, and we will try to solve this problem in the future with other types of models.…”

Section: Discussionsupporting

confidence: 53%

Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Borrero¹,

Mariscal²

2022

Mathematics

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Time series forecasting is one of the main venues followed by researchers in all areas. For this reason, we develop a new Kalman filter approach, which we call the alternative Kalman filter. The search conditions associated with the standard deviation of the time series determined by the alternative Kalman filter were suggested as a generalization that is supposed to improve the classical Kalman filter. We studied three different time series and found that in all three cases, the alternative Kalman filter is more accurate than the classical Kalman filter. The algorithm could be generalized to time series of a different length and nature. Therefore, the developed approach can be used to predict any time series of data with large variance in the model error that causes convergence problems in the prediction.

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Section: Discussionsupporting

confidence: 53%

Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Borrero¹,

Mariscal²

2022

Mathematics

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“…Mohr & Kühl (2021) investigated the barriers in AI acceptance in agriculture and applied technology acceptance model [ 32 ]. Nevertheless, Mahto et al (2021) used artificial neural network (ANN) to forecast prices of agriculture commodities and compared of performance of their model with ARIMA model for sustainable agriculture [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

2022

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There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.

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“…The past trend of btnormalΔt, is, then, informative on the position of the agent along the wave (increasing or decreasing part) of speculative bubble and enables to take a market decision even at “optimal for practitioners” lags, (hereafter OFP), i.e. l = 9–10 months, usually preferred by investors in their market decisions (Mahto et al. , 2021).…”

Section: The Detection and Forecast Of Speculative Bubblesmentioning

confidence: 99%

A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles

Ciaschini

Recchioni

2022

RBF

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PurposeThis work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.Design/methodology/approachData evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).FindingsThe empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.Originality/valueThe authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.

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Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Network

Cited by 39 publications

References 38 publications

Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

A market sentiment indicator, behaviourally grounded, for the analysis and forecast of volatility and bubbles

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