Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising occupant comfort level, by providing more accurate control HVAC (heating, ventilating, and air conditioning) system. In order to make best use different input variables, a long short-term memory (LSTM) based sequence (seq2seq) model was proposed multi-step ahead forecasting. The out-of-sam...

Abstract Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to provide proactive state information road network operators. A variety of methods predict variables the short-term can be found literature, ranging from time-series algorithms, machine learning tools and deep selective hybrid these approaches. Despite advances prediction techniques, challengin...

State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) instead shifting focus problems characterized by a large number of variables, non-linear long In last few years, majority best performing techniques for have been based on deep-learning models. However, su...

Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. To overcome the degradation caused by the loss of Differential Global Positioning System (DGPS) Pseudo-Range Correction (PRC), predictions of PRC is possible. In this paper, the Support Vector Machine (SVM) and Genetic Algorithms (GAs) will be incorpor...

This paper derives the analytic form of multi-step ahead prediction density a Gaussian GARCH(1,1) process with possibly asymmetric news impact curve in GJR class. These results can be applied when single-period returns are modeled as and interest lies at some future forecast horizon. The has been used applications an approximation to this yet unknown density; derived here shows that density, wh...

Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, mitigating flood damage. This research developed a novel methodology that involves data pre-processing an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast monthl...