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Full Description
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.
Contents
1. On Dimensionless Dissimilarity Measures for Time Series 2. The Classification Analysis of Variability of Time Series of Different Origin 3. A Comparative Study of CNN Architectures for Remaining Useful Life Estimation 4. The Analysis of Dynamical Changes and Local Seismic Activity of the Enguri Arch Dam 5. Analysis and Prediction of Daily Closing Price of Commodity Index Using Auto Regressive Integrated Moving Averages 6. Neural Networks Analysis of Suspended Sediment Transport Time Series Modeling in a River System 7. Ranking Forecasting Algorithms Using MCDM Methods: A Python Based Application 8. Rainfall Prediction Using Artificial Neural Network 9. Statistical Downscaling and Time Series Analysis for Future Scenarios of Temperature in Haridwar District, Uttarakhand
