After a brief review of various mappings of fermion pairs to bosons, we rigorously derive a general approach. Following the methods of Marumori and Otsuka, Arima, and Iachello, our approach begins with mapping states and constructs boson representations that preserve fermion matrix elements. In several cases these representations factor into finite, Hermitian boson images times a projection or ...

This research work has addressed demand side flexibility in a smart grid oriented building. The principal purpose has been to build a short term forecasting model that will predict the next hour consumption. Three advanced methods of forecasting have been investigated for this purpose, the ARIMA (Autoregressive Integrated Moving Average) model, Artificial Neural Networks (ANN) and Support Vecto...

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the ...

The aim of the paper is to investigate the use of online data and time series analysis, in order to study the dynamics of new types of research collaboration in a systematic way. Two international research teams were studied for more than three years, and quantitative data about their internet use together with observation of their collaboration patterns were gathered. Time series analysis (ARI...

The X11-ARIMA method, or X12 method based on the latter procedure, with their various variants are the most commonly procedures used for estimating the seasonally adjusted data and the trend-cycle. Both of these procedures fail to provide estimates for the variances of the estimators that they produce. In this paper we propose a simple general method, based on linear approximation, for estimati...

Article history: Accepted 16 December 2015 Available online xxxx This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models....

State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dynamic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard fu...

With service types and requirements of broadband satellite internet continuously increasing, improving QoS (Quality of Service) of satellite internet has attracted extensive attention. To reduce the impact of selfsimilarity caused by various of service traffic sources converging on satellite communication system, this paper establishes a novel model from the perspective of self-similar traffic ...

We develop and show applications of two new test statistics for deciding if one ARIMA model provides significantly better h-step-ahead forecasts than another, as measured by the difference of approximations to their mean square forecast error. The two statistics differ in the variance estimates used for normalization. Both variance estimates are consistent even when the models considered are in...

State Space Models (SSM) is a MATLAB 7.0 software toolbox for doing time series analysis by state space meth-ods. The software features fully interactive construction and combination of models, with support for univariate andmultivariate models, complex time-varying (dynamic) models, non-Gaussian models, and various standard modelssuch as ARIMA and structural time-series models. The...