Forecasting of wheat production in Haryana using hybrid time series model

Devi, M. Nirmala; Kumar, Jitendra; Malik, D. P.; Mishra, Pradeep

doi:10.1016/j.jafr.2021.100175

Cited by 19 publications

(12 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. [14] employed Box-Jenkins ARIMA and Artificial neural network (ANN) methodology was used to develop the model and estimate the forecasting of wheat production. Fenfei Gu, and Xiande Hu, [15] proposed new algorithm AAFU (ARIMA approach based on the filter updating) for energy consumption.…”

Section: Original Research Articlementioning

confidence: 99%

Time Series Prediction Using Hybrid ARIMA-ANN Models for Sugarcane

Sanjeev

Bhardwaj²

2022

IJPSS

View full text Add to dashboard Cite

Recently Hybrid model approach has led to a tremendous surge in many domains of science and engineering. In this study, we present the advantage of ANN to improve time series forecasting precision. The Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models are used to separately recognize the linear and nonlinear components in the data set respectively. In this manner, the proposed approach tactically utilizes the unique strengths ARIMA and ANN to improve the forecasting accuracy. Our hybrid method is tested on two Yamunanagar and Panipat sugarcane time series of Haryana. Results clearly indicate that Hybrid ARIMA-ANN model was better perform than ARIMA models with smaller values of RMSE and MAPE for both districts.

show abstract

Section: Original Research Articlementioning

confidence: 99%

Time Series Prediction Using Hybrid ARIMA-ANN Models for Sugarcane

Sanjeev

Bhardwaj²

2022

IJPSS

View full text Add to dashboard Cite

show abstract

“…Wheat, counted among the ‘big three’ cereal crops, is extensively cultivated in the world. It is estimated that in 2019, the world's yield of wheat was 605.99 million tons 9 . Wheat is a good source for the production of food protein.…”

Section: Introductionmentioning

confidence: 99%

“…It is estimated that in 2019, the world's yield of wheat was 605.99 million tons. 9 Wheat is a good source for the production of food protein. As a cheap and safe nutritional factor, wheat protein (mainly composed of glutenin and gliadin) has been used to produce bioactive peptides or hydrolysates with various biological functions, including antioxidant, antihypertensive, antimicrobial, anticancer and anti-inflammatory activities.…”

Section: Introductionmentioning

confidence: 99%

Identification and target of action of cholecystokinin‐releasing peptides from simulated digestion hydrolysate of wheat protein

Song,

Wang,

Shao

et al. 2023

J Sci Food Agric

View full text Add to dashboard Cite

BackgroundWheat protein intake leads to improved appetite control. However, the active components causing appetite in wheat have not fully clarified. Gut cholecystokinin (CCK) plays a vital role in appetite control. This study aimed to investigate the ability of wheat protein digest (WPD) to stimulate CCK secretion and clarify the active components and target of action.ResultsWPD was prepared by simulated gastrointestinal digestion model. WPD treatment with a concentration of 5 mg/mL significantly stimulated CCK secretion in enteroendocrine STC‐1 cells (P < 0.05). Furthermore, oral gavage with WPD in mice significantly increased plasma CCK level at 60 min (P < 0.01). Preparative C18 column separation was used to isolate peptide fractions associated with CCK secretion and peptide sequences were identified by LC‐MS/MS. A new CCK‐releasing peptide RYIVPL that could potently stimulated CCK secretion was successfully identified. After pretreatment with a specific calcium‐sensing receptor (CaSR) antagonist NPS 2143, CCK secretion induced by WPD or RYIVPL was greatly suppressed, suggesting that CaSR was involved in WPD or RYIVPL induced CCK secretion.ConclusionThe present study demonstrated that WPD had an ability to stimulate CCK secretion in vitro and in vivo, and determined that peptide RYIVPL in WPD could stimulate CCK secretion through CaSR.This article is protected by copyright. All rights reserved.

show abstract

“…Mehmood et al [16] considered an ARIMA model to be their best research device for developing and estimating time series models to forecast the production of sugar cane in Pakistan. The comparison of ARIMA and other time series models based on different types of scientific data series is now a subject of elevated research [20][21][22][23][24]. The ARIMA prediction equation is a linear equation that comprises the dependent variable and/or prediction error lags.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting of Sugarcane Production in South Asian Countries

Mishra¹,

Alakkari

Lama³

et al. 2022

Curr. Appl. Sci. Technol.

View full text Add to dashboard Cite

Sugarcane industry is of crucial importance to the South Asian countries. These countries depend heavily on agriculture and the sugarcane industry has immense potential to contribute towards its economic development. Hence, the precise and timely forecast of sugarcane production is of concern for farmers, policy makers and other stakeholders. In this manuscript, we strived to forecast the production and growth rate of this important commodity using standard statistical approaches. The ARIMA (Auto Regressive Integrated Moving Average) and ETS (Exponential Smoothing) models were applied and compared on the basis of their forecasting efficiency for South Asia countries. This study also investigated the trends in sugarcane production in the region and studies the causes of the decline in production of sugarcane in Sri Lanka and Bangladesh. Furthermore, the expected production for following 7 years was computed using both models. In addition, we also calculated the projected growth rates of sugarcane production of South Asian countries over the years 2020-2027.

show abstract

Forecasting of wheat production in Haryana using hybrid time series model

Cited by 19 publications

References 7 publications

Time Series Prediction Using Hybrid ARIMA-ANN Models for Sugarcane

Time Series Prediction Using Hybrid ARIMA-ANN Models for Sugarcane

Identification and target of action of cholecystokinin‐releasing peptides from simulated digestion hydrolysate of wheat protein

Modeling and Forecasting of Sugarcane Production in South Asian Countries

Contact Info

Product

Resources

About