Despite its importance, there has been little attention in the modeling of time series data of categorical nature in the recent past. In this paper, we present a framework based on the Pegram's [An autoregressive ...
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Despite its importance, there has been little attention in the modeling of time series data of categorical nature in the recent past. In this paper, we present a framework based on the Pegram's [An autoregressive model for multilag Markov chains. Journal of Applied Probabability 17, 350-362] operator that was originally proposed only to construct discrete AR(p) processes. We extend the Pegram's operator to accommodate categorical processes with ARMA representations. We observe that the concept of correlation is not always suitable for categorical data. As a sensible alternative, we use the concept of mutual information, and introduce auto-mutual information to define the time series process of categorical data. Some model selection and inferential aspects are also discussed. We implement the developed methodologies to analyze a time series data set on infant sleep status. (C) 2008 Elsevier B.V. All rights reserved,
Accurate prediction of wind speed is needed as the wind power directly depends upon the wind speed. Because of the complex non-stationary and nonlinear characteristics of wind speed, it is difficult to achieve good pr...
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Accurate prediction of wind speed is needed as the wind power directly depends upon the wind speed. Because of the complex non-stationary and nonlinear characteristics of wind speed, it is difficult to achieve good prediction accuracy. Compared to the prediction models that use single algorithms, hybrid models always have higher accuracy. The decomposition algorithm called Empirical Mode Decomposition (EMD) is combined with the optimization algorithm named Tabu Search (TS) and General Regression Neural Network (GRNN) to achieve high precision and is proposed in this study. The performance of the proposed approach is evaluated using wind speed datasets of different cities in India. The detail of the proposed model is given as follows: EMD (Empirical Mode Decomposition) decomposes the original datasets of wind speed into intrinsic mode functions (IMFs). A partialautocorrelationfunction determines the number of neurons in the input layer of GRNN. An intelligent algorithm namely Tabu Search is used to optimize the neural networks globally. The proposed model has better prediction accuracy in long term wind speed forecasting.
Precise evapotranspiration (ET) estimation is critical for agricultural water management, particularly in waterstressed developing countries. Vapor Pressure Deficit is one of the ET parameters that has a significant i...
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Precise evapotranspiration (ET) estimation is critical for agricultural water management, particularly in waterstressed developing countries. Vapor Pressure Deficit is one of the ET parameters that has a significant impact on its calculation (VPD). This paper forecasts VPD using ensemble learning-based modeling in eight different regions (Dakahliyah, Gharbiyah, Kafr Elsheikh, Dumyat, Port Said, Ismailia, Sharqiyah, and Qalubiyah) in Egypt. In this study, six machine learning algorithms were used: Linear Regression (LR), Additive regression trees (ART), Random SubSpace (RSS), Random Forest (RF), Reduced Error Pruning Tree (REPTree), and Quinlan's M5 algorithm (M5P). Monthly vapor pressure data were obtained from the Japanese 55-year Reanalysis JRA-55 from 1958 to 2021. The dateset has been divided into two segments: the training stage (1958-2005) and the testing stage (2006-2021). Five statistical measures were used to evaluate the model performances: correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative absolute error (RAE), and Root Relative Squared Error (RRSE), across both training and testing stages. RF model outperformed the rest of the models [CC = 0.9694;MAE = 0.0967;RMSE = 0.1252;RAE (%) = 21.7297 and RRSE (%) = 24.0356], followed closely by REPTree and RSS models. On the other hand, M5P model performance remained moderate and both LR and AR model were the worst. During the testing stage, RF outperformed the rest of the models in terms of (which statistic), followed closely by REPTree and RSS models. On the other hand, M5P performance remained moderate and both LR and AR models were the worst. This study recommended using the RF model for future hydro-climatological studies in general, and vapor pressure deficit modeling and prediction in particular. This study enables future magnitudes to be predicted, alerting the authorities and administrators involved to focus their policy-making on more specific pathways toward
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