Hyper-Erlang Battery-Life Energy Scheme in IEEE 802.16e Networks

IEEE 802.16e networks is one of the broadband wireless technologies that support multimedia services while users are in mobility. Although these users use devices that have limited battery capacity, several energy schemes were proposed to improve the battery-life. However, these schemes inappropriately capture the traffic characteristics, which lead to waste of energy and high response delay. In this paper, a Hyper-Erlang Battery-Life Energy Scheme (HBLES) is proposed to enhance energy efficiency and reduce the delay. The scheme analytically modifies idle threshold, initial sleep window and final sleep window based on the remaining battery power and the traffic pattern. It also employs a Hyper-Erlang distribution to determine the real traffic characteristics. Several simulations are carried out to evaluate the performance of the HBLES scheme and the compared scheme.  The results show that the HBLES scheme out performs the existing scheme in terms of energy consumption and response delay

Spatial pattern of rainfall events:a background study to modeling and forecasting rainfall

The study of extreme rainfall events and their spatial coverage is important in identifying areas with high and low extreme events. It has been widely known that extreme rainfall is responsible for major flash flood and landslide events that have caused significant loss of life and economic losses. Unfortunately, the dynamics of extreme rainfall events still received less concern. This study scrutinized the characteristics of extreme rainfall and their spatial coverage in Peninsular Malaysia using rain gauge data. Eight indices of climate extremes based on daily precipitation data defined and adopted by the Joint Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated. The selected indices captured the precipitation intensity, the frequency and length of heavy rainfall events. The geostatistical method of Ordinary Kriging (OK) is applied to the indices calculated. The results from OK method give a pictorial representation of the structure of extreme rainfall spatial variability which helps in deriving rainfall patterns, quantifying rainfall amounts or help in identifying areas with high risk of extreme rainfall event. This result could provide to researchers and decision makers a case study area that needs adequate attention

Long Memory Analysis of Daily Average Temperature Time Series

A time series has a long memory, in this case there is autocorrelation at long lags. If a time series display long memory, they show significant autocorrelation between observations widely separated in time. R-software has been used to analyze long memory of daily temperature series of Sokoto metropolis. The Modified Rescaled Range (R/S) statistic, the Periodogram and the Aggregated Variance Methods are used to detect long memory property of the series. Application of these tests suggests that the daily average temperature series shows evidence of long memory. Keywords: Long memory, Hurst exponent, Aggregated Variance, Modified Rescaled Range and Periodogram

Fractal Scaling Properties in Rainfall Time Series: A Case of Thiruvallur District, Tamil Nadu, India

In the present study, the features of rainfall time series (1971–2016) in 9 meteorological regions of Thiruvallur, Tamil Nadu, India that comprises Thiruvallur, Korattur_Dam, Ponneri, Poondi, Red Hills, Sholingur, Thamaraipakkam, Thiruvottiyur and Vallur Anicut were studied. The evaluation of rainfall time series is one of the approaches for efficient hydrological structure design. Characterising and identifying patterns is one of the main objectives of time series analysis. Rainfall is a complex phenomenon, and the temporal variation of this natural phenomenon has been difficult to characterise and quantify due to its randomness. Such dynamical behaviours are present in multiple domains and it is therefore essential to have tools to model them. To solve this problem, fractal analysis based on Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) analysis were employed. The fractal analysis produces estimates of the magnitude of detrended fluctuations at different scales (window sizes) of a time series and assesses the scaling relationship between estimates and time scales. The DFA and (R/S) gives an estimate known as Hurst exponent (H) that assumes self-similarity in the time series. The results of H exponent reveals typical behaviours shown by all the rainfall time series, Thiruvallur and Sholingur rainfall region have H exponent values within 0.5 < H < 1 which is an indication of persistent behaviour or long memory. In this case, a future data point is likely to be followed by a data point preceding it; Ponneri and Poondi have conflicting results based on the two methods, however, their H values are approximately 0.5 showing random walk behaviour in which there is no correlation between any part and a future. Thamaraipakkam, Thiruvottiyur, Vallur Anicut, Korattur Dam and Red Hills have H values less than 0.5 indicating a property called anti-persistent in which an increase will tend to be followed by a decrease or vice versa. Taking into consideration of such features in modelling, rainfall time series could be an exhaustive rainfall model. Finding appropriate models to estimate and predict future rainfalls is the core idea of this study for future research

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

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

Nonlinear Smooth Transition Autoregressive (STAR)–type modelling and forecasting on Malaysia Airlines (MAS) stock returns

This study aims to apply nonlinear Smooth Transition Autoregressive (STAR)-type model to the Malaysia Airlines (MAS) Stock Returns, which consists of 4450 number of observations. The data taken started from 29th August 1996 until 26th September 2014. Following the STAR strategies by Terasvirta, the diagnostic plots of linear Autoregressive (AR) model revealed that AR (3) model is adequate in modelling the MAS returns series. However, the squared residuals of Autocorrelation Function (ACF) of returns series illustrates a slight presence of correlations in the model, hence the effort to apply nonlinear model was continued. Before proceed to nonlinear STAR modelling, the identification of delay parameter in the second stage of Terasvirta need to be determined. The results of Lagrange Multiplier (LM) tests revealed that delay parameter, d=3 is the best to choose. In addition, the null hypothesis of linearity from LM test is rejected. Furthermore, from the sequence of nested hypothesis of delay parameter, d=3 indicated that LSTAR model is preferred than ESTAR model. Finally, the forecasts and comparison stages was made to compare which models are best performed in forecasting the future series of MAS returns. It proved that LSTAR model performed better in term of forecasting accuracy when compared to ESTAR and AR model

Volatility modeling of rainfall time series

Networks of rain gauges can provide a better insight into the spatial and temporal variability of rainfall, but they tend to be too widely spaced for accurate estimates. A way to estimate the spatial variability of rainfall between gauge points is to interpolate between them. This paper evaluates the spatial autocorrelation of rainfall data in some locations in Peninsular Malaysia using geostatistical technique. The results give an insight on the spatial variability of rainfall in the area, as such, two rain gauges were selected for an in-depth study of the temporal dependence of the rainfall data-generating process. It could be shown that rainfall data are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. The autocorrelation structure of the residuals and the squared residuals derived from autoregressive integrated moving average (ARIMA) models were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, and the Ljung-Box test confirmed the results. A test based on the Lagrange multiplier principle was applied to the squared residuals from the ARIMA models. The results of this auxiliary test show a clear evidence to reject the null hypothesis of no autoregressive conditional heteroskedasticity (ARCH) effect. Hence, it indicates that generalized ARCH (GARCH) modeling is necessary. An ARIMA error model is proposed to capture the mean behavior and a GARCH model for modeling heteroskedasticity (variance behavior) of the residuals from the ARIMA model. Therefore, the composite ARIMA-GARCH model captures the dynamics of daily rainfall in the study area. On the other hand, seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated

Modeling monthly rainfall time series using ETS state space and SARIMA models

The study of rainfall time series of two selected weather stations in Malaysia using various statistical methods enabled to analyse the temporal behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. SARIMA (1, 1, 2)(1, 1, 1)12, SARIMA(4, 0, 2)(1, 0, 1)12 with constant and ETS state space models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Malacca and Kuantan

Hybrid of ARIMA-GARCH modeling in rainfall time series

The dependence structure of rainfall is usually very complex both in time and space. It is shown in this paper that the daily rainfall series of Ipoh and Alorsetar are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. In most empirical modeling of hydrological time series, the focus was on modeling and predicting the mean behavior of the time series through conventional methods of an Autoregressive Moving Average (ARMA) modeling proposed by the Box Jenkins methodology. The conventional models operate under the assumption that the series is stationary that is: constant mean and either constant variance or season-dependent variances, however, does not take into account the second order moment or conditional variance, but they form a good starting point for time series analysis. The residuals from preliminary ARIMA models derived from the daily rainfall time series were tested for ARCH behavior. The autocorrelation structure of the residuals and the squared residuals were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, the Ljung-Box test confirmed the results. McLeod-Li test and a test based on the Lagrange multiplier (LM) principle were applied to the squared residuals from ARIMA models. The results of these auxiliary tests show clear evidence to reject the null hypothesis of no ARCH effect. Hence indicates that GARCH modeling is necessary. Therefore the composite ARIMA-GARCH model captures the dynamics of the daily rainfall series in study areas more precisely. On the other hand, Seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treate