Recent Advances in Time Series Forecasting
- Length: 238 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-09-08
- ISBN-10: 0367607751
- ISBN-13: 9780367607753
- Sales Rank: #0 (See Top 100 Books)
Future predictions are always a topic of interest. Precise estimates are crucial in many activities as forecasting errors can lead to big financial loss. The sequential analysis of data and information gathered from past to present is call time series analysis. This book covers the recent advancements in time series forecasting. The book includes theoretical as well as recent applications of time series analysis. It focuses on the recent techniques used, discusses a combination of methodology and applications, presents traditional and advanced tools, new applications, and identifies the gaps in knowledge in engineering applications. This book is aimed at scientists, researchers, postgraduate students and engineers in the areas of supply chain management, production, inventory planning, and statistical quality control.
Cover Half Title Series Page Title Page Copyright Page Contents Preface Editors 1. Time Series Econometrics: Some Initial Understanding 1.1 Introduction 1.1.1 Learning Objectives 1.2 Time Series, What Is It? 1.2.1 Four Components of a Time Series 1.2.2 Trend Component 1.2.3 Cyclical Component 1.2.4 Seasonal Component 1.2.5 Irregular Component 1.2.6 Time Series in Econometric Analysis 1.3 Stationary Stochastic Processes 1.4 Random Walk Phenomenon in Time Series 1.4.2 Random Walk with Drift 1.4.3 Unit Root Stochastic Process 1.5 Spurious Regression in Time Series Analysis 1.6 Need for Stationary Data Note References 2. Time Series Analysis for Modeling the Transmission of Dengue Disease 2.1 Introduction 2.2 Theory and Applications 2.2.1 Autoregressive Integrated Moving Average (ARIMA) Method 2.2.1.1 Application of ARIMA/SARIMA Modeling 2.2.2 Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX) 2.2.2.1 Application of ARIMAX/SARIMAX 2.2.3 Exponential Smoothing 2.2.3.1 Simple Exponential Smoothing (SES) 2.2.3.2 Double Exponential Smoothing (DES) 2.2.3.3 Holt-Winters Seasonal Smoothing 2.2.3.4 Application of Exponential Smoothing Method 2.2.4 Exponential Smoothing with Explanatory Variables (ETSX) 2.2.4.1 Application of Exponential Smoothing with Explanatory Variables 2.2.5 Alpha-Sutte Modeling 2.2.5.1 Applications of Alpha-Sutte Modeling 2.2.6 Time Series Decomposition 2.2.6.1 Application of Time Series Decomposition 2.2.7 Combining Modeling Approaches 2.2.7.1 Application of Combining Modeling Approaches 2.3 Conclusion and Discussion References 3. Time Series Analysis of COVID-19 Confirmed Cases in Select Countries 3.1 Introduction 3.1.1 Emergence of Coronavirus Disease 2019 (COVID-19) 3.2 Literature Review 3.3 COVID-19 Time Series Analysis 3.3.1 Fresh COVID-19 Cases 3.4 Methodology 3.5 Results and Discussion 3.5.1 Cross-correlation between New Confirmed COVID-19 Cases of Different Countries 3.5.1.1 Using the 25-Day Daily New COVID-19 Cases Since 100 New Cases Were First Reported (Growth Curve) 3.5.1.2 Using the New COVID-19 Cases Since 100 New Cases were First Reported to the Day when it Fell Back to Less than 100 Cases (Growth + Decay) 3.5.2 Dynamic Time Warping 3.6 Conclusions References 4. Bayesian Estimation of Bonferroni Curve and Zenga Curve in the Case of Dagum Distribution 4.1 Introduction 4.2 Measuring Inequality Curves using Dagum Distribution 4.3 Bayesian Estimator of Point Measure of Bonferroni Curve BD(u), 0 <u < 1 under Different Priors using Different Loss Functions 4.3.1 Bayesian Estimation of Point Measure of Bonferroni Curve BD( u), 0 <u < 1, under Mukherjee-Islam Prior 4.3.2 Bayesian Estimation of Point Measure of Bonferroni Curve BD( u), 0 <u < 1 under Uniform Prior 4.3.3 Bayesian Estimation of Point Measure of Bonferroni Curve BD( u), 0 <u < 1, under Quasi Prior 4.4 Simulation Study 4.4.1 Conclusion 4.5 Bayesian Estimator of Point Measure of Zenga Curve ZD(u), 0 < u < 1, under Different Priors using Different Loss Functions 4.6 Simulation Study 4.7 Real-Life Example References 5. Band Pass Filters and their Applications in Time Series Analyses 5.1 Introduction: Time Series 5.1.1 Smoothing 5.2 Filtering 5.2.1 Low Pass Filter 5.2.2 High Pass Filter 5.2.3 Band Pass Filter 5.2.4 Band Stop Filter 5.3 Extended Applications of Various Classes of Filters in Real Life 5.4 Future Scope References 6. Deep Learning Approaches to Time Series Forecasting 6.1 Introduction 6.2 State-of-the-Art Forecasting 6.3 Deep Learning Methods 6.3.1 Recurrent Neural Networks (RNNs) 6.3.2 Long Short-Term Memory (LSTM) 6.4 Case Study 6.5 Conclusion References 7. ARFIMA and ARTFIMA Processes in Time Series with Applications 7.1 Introduction 7.2 Modeling Persistence: A Preview 7.3 Estimation of Fractional Differencing Parameter D 7.4 Application of ARIMA to Crude Oil Data 7.5 ARTFIMA Processes 7.6 Application of ARTFIMA to Crude Oil Prices 7.7 Concluding Remarks References Appendix 8. Comparative Study of Time Series Forecasting Models for COVID-19 Cases in India 8.1 Introduction 8.1.1 General Information about COVID-19 8.1.2 Impact on India 8.1.3 Impact on Daily Activities 8.1.4 Vaccine Developments 8.1.5 Research Effort for COVID-19 8.2 Related Work 8.3 Methods 8.3.1 Support Vector Regression (SVR) 8.3.2 Vector Auto Regression (VAR) 8.3.3 Polynomial Regression (PR) 8.3.4 Recurrent Neural Network (RNN) 8.3.5 Long Short-Term Memory (LSTM) 8.3.6 Gated Recurrent Unit (GRU) 8.4 Data Analysis 8.4.1 Data Description 8.4.2 Severity Analysis 8.5 Results and Discussions 8.5.1 Experimental Set-up 8.5.2 Discussions 8.6 Conclusions References 9. Time Series Forecasting Using Support Vector Machines 9.1 Introduction 9.2 Introduction to Statistical Learning Theory 9.3 Support Vector Machine (SVM) 9.4 Empirical Risk Minimization (ERM) 9.5 Structural Risk Minimization (SRM) 9.6 Support Vector Regression (SVR) 9.7 The LS-SVM Method 9.8 The DLS-SVM Technique References 10. A Comprehensive Review of Urban Floods and Relevant Modeling Techniques 10.1 Introduction 10.2 Detailed Description of Urban Flood Models 10.2.1 Storm Water Management Model (SWMM) 10.2.2 Horton's Method 10.2.3 Background of Horton Method in the Stormwater Management Model 10.2.4 Green-Ampt Method 10.2.5 HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modeling System) 10.2.6 SCS Curve Number Method 10.2.7 Limitations of the Application 10.2.8 Snyder Unit Hydrograph 10.2.9 SCS Unit Hydrograph Model 10.2.10 Estimation of Model Parameters 10.2.11 HSPF (Hydrological Simulation Program-FORTRAN) 10.2.12 DRAINS 10.2.13 Applications 10.2.14 DR3M (Distributed Routing Rainfall-Runoff Model) 10.2.15 XP-SWMM (XP Storm Water Management Model) 10.2.16 Personal Computer Storm Water Management Model (PCSWMM) 10.2.17 Applications 10.2.18 MIKE URBAN+ 10.2.19 InfoSWMM 10.2.20 MUSIC (Model for Urban Stormwater Improvement Conceptualization) Software 10.3 Discussion and Gaps in Urban Flood Studies 10.4 Summary and Conclusions References 11. Fuzzy Time Series Techniques for Forecasting 11.1 Introduction 11.2 Fuzzy Set Theory 11.3 Fuzzy System (FS) 11.4 Membership Function 11.5 Some Features of a Membership Function 11.6 Fuzzy Logic 11.7 Fuzzy Logic Versus Bi-valued Logic 11.8 Linguistic Variables and Hedges 11.9 Fuzzy Logic Control System 11.10 Constituents of Fuzzy Logic Control System 11.11 Advantages of Fuzzy-Based Systems 11.12 Disadvantages of Fuzzy-Based Systems 11.13 Some Applications of Fuzzy-Based Systems 11.14 Fuzzy Time Series (FTS) and its Applications 11.15 Fuzzy Time Series (FTS) Model 11.16 Conclusion References 12. Artificial Neural Networks (ANNs) and their Application in Soil and Water Resources Engineering 12.1 Introduction 12.2 Biological Basis of ANNs 12.3 Model of an Artificial Neural Network 12.4 Applications 12.5 Conclusions References Index
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