In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a ...

Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurat...

I will report on a study of the usefulness of ARMA time scale algorithms to synchronize clocks on a digital network. The algorithm acquires periodic time differences between a local system clock and a remote time server by means of any of the standard message formats such as the format used by the Network Time Protocol. It models the current time difference as a linear combination of previous t...

We present a comparative study of electricity consumption predictions using the SARIMAX method (Seasonal Auto Regressive Moving Average eXogenous variables), HyFis2 model (Hybrid Neural Fuzzy Inference System) and LSTNetA (Long Short Time series Network Adapted), hybrid neural network containing GRU (Gated Recurrent Unit), CNN (Convolutional Network) dense layers, specially adapted for this cas...

Received Jul 22, 2012 Revised Oct 23, 2012 Accepted Nov 14, 2012 Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forec...

Periodically stationary times series are useful to model physical systems whose mean behavior and covariance structure varies with the season. The Periodic Auto-Regressive Moving Average (PARMA) process provides a powerful tool for modelling periodically stationary series. Since the process is non-stationary, the innovations algorithm is useful to obtain parameter estimates. Fitting a PARMA mod...

Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

During this field survey, we measured and recorded a few quality parameters of wireless communication in a substation switchyard. A microprocessor-based measurement system was used for data collection and analysis. We investigated long-term noise variation in this specific environment. Based on our measurement and post-processing analysis we conclude that the so-called Classic/Bayesian assumpti...

This paper presents a comprehensive study of ANFIS+ARIMA+IT2FLS models for forecasting the weather of Raipur, Chhattisgarh, India. For developing the models, ten year data (2000-2009) comprising daily average temperature (dry-wet), air pressure, and wind-speed etc. have been used. Adaptive Network Based Fuzzy Inference System (ANFIS) and Auto Regressive Moving Average (ARIMA) models based on In...