Performance Evaluation of a New Hybrid Multivariate Meteorological Model Analysis: A Simulation Study

Simulation is used to measure the robustness and the efficiency of the forecast-ing techniques performance over complex systems. A method for simulating multivariatetime series was presented in this study using vector autoregressive base-process. Byapplying the methodology to the multivariable meteorological time series, a simulationstudy was carried out to check for the model performance. MAPE and MAE performancemeasurements were used and the results show that the proposed method that considerpersistency in volatility gives better performance and theaccuracy error is six time smallerthan the normal hybrid model

A hybrid multivariate time series m odel for forecasting m eteorological data in peninsular malaysia

An extreme rainfall event, high temperature, haze, glacier melting, rises of sea level, and droughts are as a result of climate change. The impact of climate change may result to the devastation of the earth and life. For early preparations to face the challenges of climate change, a model that can forecast future weather variables is needed. There exist several weather models that forecast the future atmospheric data; however, the existing models which are not station-based models, hence will have an incomplete understanding of climate system of a particular case study area. To improve on the climatic modelling, this study developed a new model where the model used data collected from Alor Setar weather stations in Peninsular M alaysia by taking into consideration all the identified dynamic features of the variables. The model is an extension of multivariate time series method, namely vector autoregressive (VAR) model. Dynamic conditional correlation (DCC) model from generalised autoregressive conditional heteroscedasticity (GARCH) model was applied in this study since weather variable has high volatility and DCC model is able to capture the volatility of the model. However, because of the high persistence in the volatility, DCC model alone is not able to capture the structural changes in the volatility. To improve on the model, a joint model with hidden Markov model (HMM) is proposed whereby HM M method will consider the structural changes in the volatility that experienced high, moderate and low volatility. The findings presented that, due to neglected of structural change in volatility, the VAR multivariate time series with the hybrid of DCC model was not able to capture closely the volatility of the weather data. Nevertheless, the proposed joint model that uses the HM M to consider the structural changes in the volatility was able to capture the degree of persistence in the weather data. The out-sample forecasting accuracy gives less than ten percent of the mean absolute percentage error (MAPE) for the proposed joint model. Simulation study proves that the VAR-HMM- DCC proposed model has better result as compare to the hybrid of the conventional VAR-DCC model. The newly joint VAR-HMM-DCC model is the contribution that provides strategies for the future forecasting weather data

Meteorological multivariable approximation and prediction with classical VAR-DCC approach

The vector autoregressive (VAR) approach is useful in many situations involving model development for multivariables time series. VAR model was utilised in this study and applied in modelling and forecasting four meteorological variables. The variables are n rainfall data, humidity, wind speed and temperature. However, the model failed to address the heteroscedasticity problem found in the variables, as such, multivariate GARCH, namely, dynamic conditional correlation (DCC) was incorporated in the VAR model to confiscate the problem of heteroscedasticity. The results showed that the use of the VAR coupled with the recognition of time-varying variances DCC produced good forecasts over long forecasting horizons as compared with VAR model alone

Best fitted distribution for meteorological data in Kuala Krai

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented

Forecasting wind speed in peninsular Malaysia: An application of Arima and Arima-Garch models

In the global energy context, renewable energy sources such as wind is considered as a credible candidate for meeting new energy demands and partly substituting fossil fuels. Modelling and forecasting wind speed are noteworthy to predict the potential location for wind power generation. An accurate forecasting of wind speed will improve the value of renewable energy by enhancing the reliability of this natural resource. In this paper, the wind speed data from year 1990 to 2014 in 18 meteorological stations throughout Peninsular Malaysia were modelled using the Autoregressive Integrated Moving Average (ARIMA) to forecast future wind speed series. The Ljung-Box test was used to determine the presence of serial autocorrelation, while the Engle’s Lagrange Multiplier (LM) test was used to investigate the presence of Autoregressive Conditional Heteroscedasticity (ARCH) effect in the residual of the ARIMA model. In this study, three stations showed good fit using the ARIMA modelling since no serial correlation and ARCH effect were present in the residuals of the ARIMA model, while the ARIMA-GARCH had proven to precisely capture the nonlinear characteristic of the wind speed daily series for the remaining stations. The forecasting accuracy measure used was based on the value of root mean square error (RMSE) and mean absolute percentage error (MAPE). Both ARIMA and ARIMA-GARCH model proposed provided good forecast accuracy measure of wind speed series in Peninsular Malaysia. These results will help in providing a quantitative measure of wind energy available in the potential location for renewable energy conversion

Meteorological multivariable approximation and prediction with classical VAR-DCC approach

The vector autoregressive (VAR) approach is useful in many situations involving model development for multivariables time series. VAR model was utilised in this study and applied in modelling and forecasting four meteorological variables. The variables are n rainfall data, humidity, wind speed and temperature. However, the model failed to address the heteroscedasticity problem found in the variables, as such, multivariate GARCH, namely, dynamic conditional correlation (DCC) was incorporated in the VAR model to confiscate the problem of heteroscedasticity. The results showed that the use of the VAR coupled with the recognition of time-varying variances DCC produced good forecasts over long forecasting horizons as compared with VAR model alone

Coherent mortality model in a state-space approach

Mortality improvements that have recently become apparent in most developing countries have significantly shaped queries on forecast divergent between populations in recent years. Therefore, to ensure a more coherent way of forecasting, previous researchers have proposed multi-population mortality model in the form of independent estimation procedures. However, similar to single-population mortality model, such independent approaches might lead to inaccurate prediction interval. As a result of this inaccurate mortality forecasts, the life expectancies and the life annuities that the mortality model aims to generate is underestimated. In this study, we propose another new extension of the multi-population mortality model in a joint estimation approach by recasting the model into a state-space framework. A combination of augmented Li-Lee and O’Hare-Li methods are employed, before we transform the proposed model into a state-space formulation. In addition, this study incorporates the quadratic age effect parameter to the proposed model to better capture the younger ages mortality. We apply the method to gender and age-specific data for Malaysia. The results show that our latter framework brings a significant contribution to the multi-population mortality model due to the incorporation of joint-estimate and quadratic age effect parameters into the model’s structure. Consequently, the proposed model improves the mortality forecast accuracy

Instantaneous causality approach to meteorological variables bond

This paper aimed to establish a relationship between the selective meteorological variables such as temperature, humidity, wind speed, and rainfall in which contribute to the climate change in Peninsular Malaysia. Regression analysis, instantaneous causality, and impulse response function analysis were applied to the variables. The results revealed that humidity is positively related to rainfall and there is a strong relation between them. The instantaneous causality between wind speed and other variables concluded that the capability to predict the series of temperature, humidity, and rainfall based on the histories of all observable variables is unaffected by the omission of wind speed’s history