Time Series Prediction

This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators.

Universal codes efficiently compress sequences generated by stationary and ergodic sources with unknown statistics, and they were originally designed for lossless data compression. In the meantime, it was realized that they can be used for solving important problems of prediction and statistical analysis of time series, and this book describes recent results in this area.

This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems.

Learn how to forecast cryptocurrency market with Prophet model, time series decomposition, Random Forest, and XGBoost

The subject of time series analysis has intimate connections with a wide range of topics, among which may be named statistical communication theory, the theory of prediction and control, and the statistical analysis of time series data. The last of these is to some extent subsidiary to the other two, since its purpose, in part at least, must be to provide the information essential to the application of those theories. However, it also has an existence of its own because of its need in fields (e.g., economics) in which at present well-developed, exact theories of control are not possible.