A bootstrap procedure for constructing prediction bands a stationary functional time series is proposed. The exploits general vector autoregressive representation of the time-reversed Fourier coefficients appearing in Karhunen–Loève process. It generates backward-in-time replicates that adequately mimic dependence structure underlying process model-free way and have same conditionally fixed cur...

Chaotic time series have been involved in many fields of production and life, so their prediction has a very important practical value. However, due to the characteristics chaotic series, such as internal randomness, nonlinearity, long-term unpredictability, most methods cannot achieve high-precision intermediate or predictions. Thus, an (ILTP) method for n-dimensional is proposed solve this pr...

prediction of precipitation is very important. regarding to the non- linear relationships and uncertainty of models, there is no superior and persuasive model among various proposed models to simulate precise precipitation and its amount. wavelet is one of the novel and very effective methods in time series and signals analyzing, that has been considered in the field of hydrology in recent year...

Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in ...

The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncatio...

A temperature prediction method of Insulated Gate Bipolar Transistor (IGBT) module based on autoregressive moving average model is proposed. Historical and current temperature datum of IGBT module is indispensable to the ARMA method, temperature time series is obtained by uniform sampling, and autoregressive (AR) model is constructed. Temperature time series prediction of IGBT module is realize...

In time series prediction, historical data are used as the basis of estimating future outcomes. Many methods including statistical predictive models and artificial intelligence (AI) based models have been proposed for time series prediction. When dealing with limited information, researchers tend to seek for AI-based approaches as statistical models require large samples to determine the underl...

An event called prediction in a time series is more important for geophysics and economy problems. The time series data mining is a combination field of time series and data mining techniques. The historical data are collected which has follow the time series methodology, combine the data mining for preprocessing and finally apply the fuzzy logic rules to predict the impact of earthquake. Earth...