The key component in forecasting demand and consumption of resources in a supply
network is an accurate prediction of real-valued time series. Indeed, both service …

Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …

Large volumes of event data are becoming increasingly available in a wide variety of
applications, such as healthcare analytics, smart cities and social network analysis. The …

With recent advances in computing technology, massive amounts of data and information
are being constantly accumulated. Especially in the field of finance, we have great …

Traffic flow forecasting is a necessary part in the intelligent transportation systems in
supporting dynamic and proactive traffic control and making traffic management plan …

We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network
(ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short …

The real-time prediction of ship behavior plays an important role in navigation and intelligent
collision avoidance systems. This study developed an online real-time ship behavior …

In this paper we focus our attention on the long-term load forecasting problem, that is the
prediction of energy consumption for several months ahead (up to one or more years) …

Improving forecasting especially time series forecasting accuracy is an important yet often
difficult task facing decision makers in many areas. Combining multiple models can be an …

Particle swarm optimisation (PSO) has been enormously successful. Within little more than a
decade hundreds of papers have reported successful applications of PSO. In fact, there are …