Metrine chonge - Academia.edu (original) (raw)
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Papers by Metrine chonge
In this paper we fit a time series model that best describes the rainfall pattern of Uasin Gishu ... more In this paper we fit a time series model that best describes the rainfall pattern of Uasin Gishu county from the general ARIMA family and generate the values (p,d,q)(P,D,Q) s. The model that best fitted the Kapsoya historical rainfall data was SARIMA (0,0,0)(,0,1,2) 12. This model is used to forecast average expected monthly rainfall statistics for two years. For verification and data fitting to the model, R computer software was employed. The data used is real rainfall data from Kapsoya meteorological station in Uasin Gishu County.
IOSR Journal of Mathematics, 2016
In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov cha... more In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov chain that best describes the rainfall pattern of Bungoma County (Western Kenya).This is achieved by Markov chain theory and estimation of probabilities of the chain by MLE. Also computed is the distribution of the length of each spells; wet, medium wet and dry from which the central moments of the rainfall pattern are computed. The model developed is applied to rainfall data from Bungoma meteorological station. A three by three transition matrix is obtained and used to predict the weather pattern. It is observed that if everything remains constant, prediction can be certain at the twelfth year as the matrix show stationarity. The three states are recurrent, non-null and a periodic hence forming an ergodic chain.
In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov cha... more In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov chain that best describes the rainfall pattern of Bungoma County (Western Kenya).This is achieved by Markov chain theory and estimation of probabilities of the chain by MLE. Also computed is the distribution of the length of each spells; wet, medium wet and dry from which the central moments of the rainfall pattern are computed. The model developed is applied to rainfall data from Bungoma meteorological station. A three by three transition matrix is obtained and used to predict the weather pattern. It is observed that if everything remains constant, prediction can be certain at the twelfth year as the matrix show stationarity. The three states are recurrent, non-null and a periodic hence forming an ergodic chain.
In this paper we fit a time series model that best describes the rainfall pattern of Uasin Gishu ... more In this paper we fit a time series model that best describes the rainfall pattern of Uasin Gishu county from the general ARIMA family and generate the values (p,d,q)(P,D,Q) s. The model that best fitted the Kapsoya historical rainfall data was SARIMA (0,0,0)(,0,1,2) 12. This model is used to forecast average expected monthly rainfall statistics for two years. For verification and data fitting to the model, R computer software was employed. The data used is real rainfall data from Kapsoya meteorological station in Uasin Gishu County.
IOSR Journal of Mathematics, 2016
In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov cha... more In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov chain that best describes the rainfall pattern of Bungoma County (Western Kenya).This is achieved by Markov chain theory and estimation of probabilities of the chain by MLE. Also computed is the distribution of the length of each spells; wet, medium wet and dry from which the central moments of the rainfall pattern are computed. The model developed is applied to rainfall data from Bungoma meteorological station. A three by three transition matrix is obtained and used to predict the weather pattern. It is observed that if everything remains constant, prediction can be certain at the twelfth year as the matrix show stationarity. The three states are recurrent, non-null and a periodic hence forming an ergodic chain.
In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov cha... more In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov chain that best describes the rainfall pattern of Bungoma County (Western Kenya).This is achieved by Markov chain theory and estimation of probabilities of the chain by MLE. Also computed is the distribution of the length of each spells; wet, medium wet and dry from which the central moments of the rainfall pattern are computed. The model developed is applied to rainfall data from Bungoma meteorological station. A three by three transition matrix is obtained and used to predict the weather pattern. It is observed that if everything remains constant, prediction can be certain at the twelfth year as the matrix show stationarity. The three states are recurrent, non-null and a periodic hence forming an ergodic chain.