http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Computational intelligence-based time series analysis
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 too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.
Preface xi List of Figures xiii List of Tables xix List of Contributors xxi List of Abbreviations xxv 1 On Dimensionless Dissimilarity Measures for Time Series 1 1.1 Introduction 1 1.2 Classical Dissimilarity Measures 3 1.3 Classical Entropy-type Dissimilarity Measures 4 1.4 Dissimilarity Measures for Time Series Data 5 1.4.1 Standard Dissimilarity Measures 6 1.4.2 Advanced Dissimilarity Measures 12 1.5 Conclusions 15 2 The Classification Analysis of Variability of Time Series of Different Origin 19 2.1 Introduction 20 2.2 Used Datasets and Methods of Analysis 21 2.3 Results and Discussions 25 2.4 Summary 33 3 A Comparative Study of CNN Architectures for Remaining Useful Life Estimation 37 3.1 Introduction 38 3.2 CNN Architectures and Hyperparameters 40 3.3 Numerical Experiments 42 3.3.1 Dataset 42 3.3.2 Pre-processing 43 3.3.2.1 Labelling 44 3.3.2.2 Scaling of data 44 3.3.2.3 Splitting of data 44 3.3.2.4 Time series to recurrence plots 45 3.4 Results 46 3.5 Conclusion 47 4 The Analysis of Dynamical Changes and Local Seismic Activity of the Enguri Arch Dam 53 4.1 Introduction 54 4.2 Main Text 56 4.2.1 Methods and Results 56 4.3 Conclusion 61 5 Analysis and Prediction of Daily Closing Price of Commodity Index Using Auto Regressive Integrated Moving Averages 65 5.1 Introduction 66 5.2 Literature Review 67 5.3 Objectives and Study 69 5.4 Data and Methodology 69 5.5 Data Decomposition 69 5.5.1 Seasonality 69 5.5.2 Trend 69 5.5.3 Cyclicity 70 5.6 Augmented Dicky Fuller (ADF) Test 70 5.6.1 Auto-correlation function (ACF) 71 5.6.2 Partial Autocorrelation Function (PACF) 73 5.6.3 ARIMA Model 73 5.7 Results and Analysis 74 5.8 Conclusion and Future Work 77 6 Neural Networks Analysis of Suspended Sediment Transport Time Series Modeling in a River System 81 6.1 Introduction 82 6.2 Artificial Neural Networks 83 6.3 Hydrological Study Area 85 6.4 Methodology 87 6.4.1 Mathematics of SRC 89 6.5 Results 89 6.6 Hysteresis of Sediment Transport Process 93 6.7 Conclusions 94 6.8 Acknowledgements 94 7 Ranking Forecasting Algorithms Using MCDM Methods: A Python Based Application 99 7.1 Introduction 100 7.2 Review of Literature 100 7.2.1 Analytic Hierarchy Process (AHP) 100 7.2.2 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) 101 7.2.3 VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) 102 7.2.4 Time Series Analysis 102 7.3 Error Measurements and Forecasting 103 7.3.1 Holt-Winter 105 7.3.2 Autoregressive Integrated Moving Average (ARIMA) 105 7.3.3 SARIMA 106 7.3.4 ARIMA integrating Single Judgement Adjustment 106 7.3.5 ARIMA integrating Collaborative Judgement Adjustment 106 7.4 Multi Criteria Decision Making 107 7.4.1 Multiple Attribute Decision Making (MADM) 107 7.4.2 Multiple Objective Decision Making (MODM) 108 7.5 MCDM Methods 108 7.5.1 The AHP Method 108 7.5.2 The TOPSIS Method 110 7.5.3 The VIKOR Method 112 7.6 Framework of the Problem 114 7.7 Implementation Using Python Programming Language 115 7.7.1 Determining the criteria weights using AHP 115 7.7.2 Ranking Alternatives using TOPSIS method 116 7.7.3 Ranking Alternatives using VIKOR method 118 7.8 Result Analysis 119 7.9 Conclusion 120 7.10 Appendix 121 8 Rainfall Prediction Using Artificial Neural Network 127 8.1 Introduction 127 8.2 Materials and Method 129 8.2.1 Input and Output Data Selection 129 8.2.2 Input Data Training 129 8.2.3 Validation and Testing 129 8.2.4 Artificial Neural Network Architecture 130 8.3 Result 131 8.4 Discussion 135 8.5 Comparision of ANN Model with Regresion 136 8.6 Conclusion 140 9 Statistical Downscaling and Time Series Analysis for Future Scenarios of Temperature in Haridwar District, Uttarakhand 143 9.1 Introduction 144 9.2 Study Area 145 9.3 Data Used and Methodology 146 9.3.1 Data Used 146 9.3.2 Methodology 147 9.4 Results and Discussion 147 9.4.1 Regression Method 148 9.4.2 Predictor Variables Selection 148 9.4.3 Calibration and Validation Results 148 9.4.4 Future Emission Scenarios 152 9.5 Conclusion 156 Index 159 About the Editors 161.
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 of Information Technology he worked as an assistant professor at ITM University, Gurgaon, India. He has been a Faculty Member for around eleven years and has taught several core courses in applied mathematics and soft computing at undergraduate and master levels. His major research interests include soft computing and nature inspired optimization. He has published more than 38 research papers in national and international journals of repute. He is the Associate Editor for International Journal of Mathematical, Engineering and Management Sciences, ESCI and SCOPUS indexed journals. He is the editor of the book Computational Intelligence: Theoretical Advances and Advanced Applications published by Walter de Gruyter GmbH & Co KG. He has also published seven book chapters in reputed book series. Dr Bisht is a member of the International Association of Engineers in Hong Kong and Soft Computing Research Society, India. He has been awarded for outstanding contributions in reviewing by the editors of Applied Soft Computing Journal, Elsevier. Professor (Dr) Mangey Ram received his PhD with a major in Mathematics and minor in Computer Science from G. B. Pant University of Agriculture and Technology, Pantnagar, India. He has been a Faculty Member for around twelve years and has taught several core courses in pure and applied mathematics at undergraduate, postgraduate, and doctorate levels. He is currently the Research Professor at Graphic Era (Deemed to be University), Dehradun, India. Before joining Graphic Era, he was a Deputy Manager (Probationary Officer) with the Syndicate Bank for a short period. He is Editor-in-Chief of International Journal of Mathematical, Engineering and Management Sciences, Journal of Reliability and Statistical Studies, Editor-in-Chief of six book series with Elsevier, CRC Press-A Taylor and Frances Group, Walter De Gruyter Publisher Germany, River Publishers and the Guest Editor and Member of the editorial board of various journals. He has published 250 plus research publications (journal articles/books/book chapters/conference articles) in IEEE, Taylor & Francis, Springer, Elsevier, Emerald, World Scientific publications and many other national and international journals and conferences. Also, he has published more than 50 books (authored/edited) with international publishers like Elsevier, Springer Nature, CRC Press-A Taylor and Frances Group, Walter De Gruyter Publisher Germany, River Publishers. His fields of research are reliability theory and applied mathematics. Dr Ram is a Senior Member of the IEEE, Senior Life Member of the Operational Research Society of India, the Society for Reliability Engineering, Quality and Operations Management in India, Indian Society of Industrial and Applied Mathematics, He has been a member of the organizing committee of a number of international and national conferences, seminars, and workshops. He was conferred with the Young Scientist Award by the Uttarakhand State Council for Science and Technology, Dehradun in 2009. He was awarded the Best Faculty Award in 2011; Research Excellence Award in 2015; and recently Outstanding Researcher Award in 2018 for his significant contributions in academics and research at Graphic Era (Deemed to be University), Dehradun, India.
[Place of publication not identified] : RIVER PUBLISHERS, 2022
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