Computational Intelligence-based Time Series Analysis

• Main Author: Bisht, Dinesh C. S
• Format: eBook
• Language: English
• Published: Aalborg River Publishers 2022
• Edition: 1
• Series: River Publishers series in automation, control and robotics
• Subjects: Computational intelligence; Computer Science (General); Digital Signal Processing; ElectricalEngineeringnetBASE; ENGnetBASE; SCI-TECHnetBASE; STMnetBASE; Time-series analysis; auto-regressive integrated moving average; time series analysis; artificial neural network; KLD; Haridwar district; Tamil Nadu; Closing Price; commodity index; Multiple Linear Regression; Hurst Exponent; heart rate variability; entropy-type dissimilarity measures; Convolutional Neural Network architectures; MFDFA; local seismic activity; ADF Test; Augmented Dicky Fuller test; classical dissimilarity measures; Hydrological Gauging Station; ARIMA Model; sediment transport process; Multi-layered Feed Forward Neural Network; Sediment Concentration; Non-beneficial Criterion; MD Value; Partial Autocorrelation Function; computational intelligence; auto-correlation function; Enguri Arch Dam; Ann Model; BPA; TOPSIS Method; Back Propagation; Dissimilarity Measures; daily temperatures; Relative Entropy; Henon Attractors; daily closing price; rainfall prediction; hyperparameter tuning; Back Propagation Algorithm; Short Datasets; Pair-wise Comparison Matrix
• ISBN: 9788770224178; 877022417X
• DOI: 10.1201/9781003337676

The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of machine learning. There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or are too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.