Traffic prediction constitutes a hot research topic of network metrology. MultiStep ahead prediction allows to predict more values in the future. Then, the result can be used to act proactively in many prediction applications. In this work, the AutoRegressive Integrated Moving Average (ARIMA) model and the linear minimum mean square error (LMMSE) are used for multiStep predicting. Via experimen...

Computational intelligence approaches to multiple-step-ahead forecasting rely either on iterated one-step-ahead predictors or direct predictors. In both cases the predictions are obtained by means of multi-input single-output modeling techniques. This paper discusses the limits of single-output approaches when the predictor is expected to return a long series of future values and presents a mul...

A dependable long-term hydrologic prediction is essential to planning, designing and 12 management activities of water resources. A three-stage indirect multi-step-ahead prediction model, 13 which combines dynamic spline interpolation into multilayer adaptive time-delay neural network 14 (ATNN), is proposed in this study for the long term hydrologic prediction. In the first two stages, a 15 gro...

Multistep-ahead prediction is the task of predicting a sequence of values in a time series. A typical approach, known as multi-stage prediction, is to apply a predictive model step-by-step and use the predicted value of the current time step to determine its value in the next time step. This paper examines two alternative approaches known as independent value prediction and parameter prediction...

Forecasting data from a time series is to make predictions for the future from available data. Thus, such a problem can be viewed as a traditional data mining problem because it is to extract rules for prediction from available data. There are two kinds of forecasting approaches. Most traditional forecasting approaches are based on all available data including the nearest data and far away data...

Epidemics of influenza are major public health concerns. Since prediction always relies on the weekly clinical or laboratory surveillance data, typically Influenza-like illness (ILI) rate series, accurate multi-step-ahead predictions using ILI series is great importance, especially, to potential coming outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machi...