Agricultural price information needs for decision-making at all levels are increasing due to glob... more Agricultural price information needs for decision-making at all levels are increasing due to globalization and market
integration. Agricultural price forecasting is one of the challenging areas of time series analysis due to its strong
dependence on biological processes. In this paper, an empirical mode decomposition based neural network model is
employed for potato price forecasting. The daily potato wholesale price series from Delhi market was decomposed into
eight independent intrinsic modes (IMFs) and one residue with different frequencies.Then anartificial neural network
with single hidden layer was constructed to forecast these IMFs and residue component individually. Finally, the
prediction results of all IMFs including residue are aggregated to formulate an ensemble output for the original price
series. Empirical results demonstrated that the proposed ensemble model outperforms a single model in terms of root
mean square error and directional prediction statistics.
Modelling growth of an animal is a complex process, because it requires describing longitudinal m... more Modelling growth of an animal is a complex process, because it requires describing longitudinal measurements with few parameters with biological interpretation. With longitudinal data, the variance of observations may increase with time (age), and repeated measurements of an individual over time are correlated. The non-independence of data violates a key assumption underlying many statistical procedures and has been ignored in most traditional non-linear fixed effect models. A solution to this problem is the use of non-linear mixed effect models (NLMM). A NLMM makes it possible to account for random covariates before testing for fixed effects and control autocorrelation in repeated measures. In this study, growth data of Goat has been used. Attempt has been made to develop the Von-bertalanffy mixed model. Logistic, Gompertz and Von-bertalanffy fixed and mixed models have also been explored for these data. Comparison of the models i.e. between fixed and mixed type of the same model and among different fixed and mixed models has been attempted. The goodness of fit statistics like i.e. Mean Square Error (MSE) and Root Mean Square Error (RMSE) of the fitted models has been computed. The parameters of the best fitted models along with their corresponding standard error are estimated. The performance of mixed effect models was found to be better than the fixed effect model. Specifically, under the category of mixed effect model, the Logistic model out performed over the other types that were considered in the study.
Agricultural price information needs for decision-making at all levels are increasing due to glob... more Agricultural price information needs for decision-making at all levels are increasing due to globalization and market
integration. Agricultural price forecasting is one of the challenging areas of time series analysis due to its strong
dependence on biological processes. In this paper, an empirical mode decomposition based neural network model is
employed for potato price forecasting. The daily potato wholesale price series from Delhi market was decomposed into
eight independent intrinsic modes (IMFs) and one residue with different frequencies.Then anartificial neural network
with single hidden layer was constructed to forecast these IMFs and residue component individually. Finally, the
prediction results of all IMFs including residue are aggregated to formulate an ensemble output for the original price
series. Empirical results demonstrated that the proposed ensemble model outperforms a single model in terms of root
mean square error and directional prediction statistics.
Modelling growth of an animal is a complex process, because it requires describing longitudinal m... more Modelling growth of an animal is a complex process, because it requires describing longitudinal measurements with few parameters with biological interpretation. With longitudinal data, the variance of observations may increase with time (age), and repeated measurements of an individual over time are correlated. The non-independence of data violates a key assumption underlying many statistical procedures and has been ignored in most traditional non-linear fixed effect models. A solution to this problem is the use of non-linear mixed effect models (NLMM). A NLMM makes it possible to account for random covariates before testing for fixed effects and control autocorrelation in repeated measures. In this study, growth data of Goat has been used. Attempt has been made to develop the Von-bertalanffy mixed model. Logistic, Gompertz and Von-bertalanffy fixed and mixed models have also been explored for these data. Comparison of the models i.e. between fixed and mixed type of the same model and among different fixed and mixed models has been attempted. The goodness of fit statistics like i.e. Mean Square Error (MSE) and Root Mean Square Error (RMSE) of the fitted models has been computed. The parameters of the best fitted models along with their corresponding standard error are estimated. The performance of mixed effect models was found to be better than the fixed effect model. Specifically, under the category of mixed effect model, the Logistic model out performed over the other types that were considered in the study.
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integration. Agricultural price forecasting is one of the challenging areas of time series analysis due to its strong
dependence on biological processes. In this paper, an empirical mode decomposition based neural network model is
employed for potato price forecasting. The daily potato wholesale price series from Delhi market was decomposed into
eight independent intrinsic modes (IMFs) and one residue with different frequencies.Then anartificial neural network
with single hidden layer was constructed to forecast these IMFs and residue component individually. Finally, the
prediction results of all IMFs including residue are aggregated to formulate an ensemble output for the original price
series. Empirical results demonstrated that the proposed ensemble model outperforms a single model in terms of root
mean square error and directional prediction statistics.
parameters with biological interpretation. With longitudinal data, the variance of observations may increase with time (age), and
repeated measurements of an individual over time are correlated. The non-independence of data violates a key assumption underlying
many statistical procedures and has been ignored in most traditional non-linear fixed effect models. A solution to this problem is the
use of non-linear mixed effect models (NLMM). A NLMM makes it possible to account for random covariates before testing for
fixed effects and control autocorrelation in repeated measures. In this study, growth data of Goat has been used. Attempt has been
made to develop the Von-bertalanffy mixed model. Logistic, Gompertz and Von-bertalanffy fixed and mixed models have also been
explored for these data. Comparison of the models i.e. between fixed and mixed type of the same model and among different fixed
and mixed models has been attempted. The goodness of fit statistics like i.e. Mean Square Error (MSE) and Root Mean Square Error
(RMSE) of the fitted models has been computed. The parameters of the best fitted models along with their corresponding standard
error are estimated. The performance of mixed effect models was found to be better than the fixed effect model. Specifically, under
the category of mixed effect model, the Logistic model out performed over the other types that were considered in the study.
integration. Agricultural price forecasting is one of the challenging areas of time series analysis due to its strong
dependence on biological processes. In this paper, an empirical mode decomposition based neural network model is
employed for potato price forecasting. The daily potato wholesale price series from Delhi market was decomposed into
eight independent intrinsic modes (IMFs) and one residue with different frequencies.Then anartificial neural network
with single hidden layer was constructed to forecast these IMFs and residue component individually. Finally, the
prediction results of all IMFs including residue are aggregated to formulate an ensemble output for the original price
series. Empirical results demonstrated that the proposed ensemble model outperforms a single model in terms of root
mean square error and directional prediction statistics.
parameters with biological interpretation. With longitudinal data, the variance of observations may increase with time (age), and
repeated measurements of an individual over time are correlated. The non-independence of data violates a key assumption underlying
many statistical procedures and has been ignored in most traditional non-linear fixed effect models. A solution to this problem is the
use of non-linear mixed effect models (NLMM). A NLMM makes it possible to account for random covariates before testing for
fixed effects and control autocorrelation in repeated measures. In this study, growth data of Goat has been used. Attempt has been
made to develop the Von-bertalanffy mixed model. Logistic, Gompertz and Von-bertalanffy fixed and mixed models have also been
explored for these data. Comparison of the models i.e. between fixed and mixed type of the same model and among different fixed
and mixed models has been attempted. The goodness of fit statistics like i.e. Mean Square Error (MSE) and Root Mean Square Error
(RMSE) of the fitted models has been computed. The parameters of the best fitted models along with their corresponding standard
error are estimated. The performance of mixed effect models was found to be better than the fixed effect model. Specifically, under
the category of mixed effect model, the Logistic model out performed over the other types that were considered in the study.