Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting ...

INTRODUCTION Insomnia is a condition that affects nearly everyone at some point in life. Up to 40% of American adults have intermittent insomnia, and 10% to 15% have a chronic sleep problem.1 The main presentation of insomnia is difficulty with either falling asleep or maintaining sleep throughout the night. Complaints of insomnia tend to increase with age. Elderly people report trouble falling...

Supply chain inventories are prone to fluctuations and instability. Known as the bullwhip effect, small variations in the end item demand create oscillations that amplify throughout the chain. By using system dynamics simulation, we investigate some of the structural sources of the bullwhip effect, and explore the effectiveness of information sharing to eliminate the undesirable fluctuations. E...

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or ‘signal’) is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or ‘y-values’) at the knots of the spline. The na...

Due to technological advances, vast data sets (e.g. big data) are increasing now days. Big Data a new term; is used to identify the collected datasets. But due to their large size and complexity, we cannot manage with our current methodologies or data mining software tools to extract those datasets. Such datasets provide us with unparalleled opportunities for modelling and predicting of future ...

In this paper we investigate the multi-period forecast performance of a number of empirical selfexciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, amongst other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non-linearity’ characterises the future, and compare the forecast...

HIV/AIDS has reached a pandemic level across the world with more than 33 million people who are living with HIV. In the United States, more than half a million people have been victims of AIDS. This study investigates the most vulnerable racial minority population (the African Americans) in the United States and the second least affected (the Caucasians) in order to predict the trends of the ep...

This paper evaluates six published data fusion strategies for hydrological forecasting based on two contrasting catchments: the River Ouse and the Upper River Wye. The input level and discharge estimates for each river comprised a mixed set of single model forecasts. Data fusion was performed using: arithmetic-averaging, a probabilistic method in which the best model from the last time step is ...

The temporal variations of the ionospheric critical frequency foF2 is a typical example of a time series in which deterministic variations at various time scales and nonstationary stochastic variations are involved. In a series of journal and conference papers we have obtained various modelling, prediction and forecast algorithms for foF2 variations over Europe, based on data from about 15 iono...

Response Surface Methodology is an optimization tool used for modelling works and has been widely modelling, optimizing, identifying the interrelationship between input parameters output variables. However, it yet to be discovered as a green roof performance. This paper aims explore feasibility of in Minitab investigate hydrological performance terms peak runoff, attenuation, water retention. I...