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Grammar-Based Feature Generation for Time-Series Prediction (SpringerBriefs in Computational Intelligence)
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Computational Intelligence-based Time Series Analysis
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Computational Intelligence-based Time Series Analysis
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Computational Intelligence-based Time Series Analysis (River Publishers Series in Automation, Control, and Robotics)
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Computational Intelligence-based Time Series Analysis
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr Dinesh C. S. Bisht received his PhD with a major in Mathematics and a minor in Electronics and Communication Engineering from G. B. Pant University of Agriculture and Technology, Uttarakhand, India. Before joining the Jaypee Institute.
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Add to basketBuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
