Hybrid ARIMA-ANN Modelling for Forecasting the Price of  Robusta Coffee in India

Naveena, K.; Singh, Subedar; Rathod, Santosha; Singh, Abhishek

doi:10.20546/ijcmas.2017.607.207

Cited by 29 publications

(25 citation statements)

References 6 publications

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“…The popular prediction evaluation methods like Coefficient of determination (R 2 ), Root mean squared error (RMSE) and Mean absolute percentage error (MAPE) used to evaluates the accuracy of prediction models (Naveena et al, 2017)…”

Section: Methodsmentioning

confidence: 99%

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Shruthi

Siddaraju

Naveena

et al. 2021

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Background: Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn’t work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models.Methods: Field experiment was conducted using 98 soybean core population through augmented design.18 morphometric traits obtain from soybean core population were considered under the study as regressors.Multiple linear regression (MLR), Principle component Regression (PCR), Regression tree and Random Forest models were compared to select traits based on prediction accuracy.Result: All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean.Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis.

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Section: Methodsmentioning

confidence: 99%

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Shruthi

Siddaraju

Naveena

et al. 2021

Self Cite

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“…(ZHANG, 2003) stated that it is more effective to combine individual forecasts that are based on different information sets. Also (NAVEENA, 2017) concluded that the hybrid method which combines linear and non-linear models can be an effective way to improve forecasting performance. (RATHNAYAKA, 2015) suggested that the hybrid model is more significant and gives the best solution for predicting future predictions under the high volatility fluctuations than traditional forecasting approaches.…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Time series forecasting of styrene price using a hybrid ARIMA and neural network model

Ghahnavieh¹

2019

Ind. Jour. Manag. & Prod.

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Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.

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“…Some research showed that the hybrid ARIMA-ANN model improved the accuracy of forecasting the resource usage in server virtualization as compared to ARIMA and ANN separately [18]. Another study also suggested that the hybrid ARIMA-ANN model has the best model for forecasting Indian Robusta coffee projection [19]. In recent years, machine learning techniques are developed to predict the financial time series data with greater accuracy [20].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Selvakumar¹,

Satpathi²,

Kumar³

et al. 2021

ujaf

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The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

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Hybrid ARIMA-ANN Modelling for Forecasting the Price of Robusta Coffee in India

Cited by 29 publications

References 6 publications

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Trait Based Modelling Approach for Selection of Elite Germplasm Accessions in Soybean [Glycine max (L). Merrill]

Time series forecasting of styrene price using a hybrid ARIMA and neural network model

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

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