book

Artificial Intelligence in Forecasting

Name: Artificial Intelligence in Forecasting
ISBN: 9781040051528

by Sachi Mohanty, Preethi Nanjundan, Tejaswini Kar

July 2024

Beginner to intermediate

364 pages

13h

English

CRC Press

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Cover Page
Title Page
Copyright Page
Preface
Contents
1. Pursuits of Forecasting: Revisiting the Claims of Artificial Intelligence
1 Introduction2 Basic Concepts2.1 Quantitative Forecasting2.2 Qualitative Forecasting3 Traditional Forecasting Methods: The Methods used Initially1 Collective Opinion Method2 Survey Method3 Market Experiment Method4 Revisiting Artificial Intelligence5 Seeing Through Types of Artificial Intelligence (AI)1 Narrow AI or Weak AI2 Artificial General Intelligence3 Strong Artificial Intelligence1 Reactive Machines2 Limited Memory3 Theory of Mind4 Self-Awareness6 Stock Market Forecasting Using Current AI Formulae: Relooking at Demand Forecasting6.1 Demand Forecasting6.2 AI Methods to Forecast Stock Prices2 Forecasting Stock Prices Using Linear Regression and Artificial Neural Networks7 Weather Forecasting Using AI-based Techniques1 Forecasting Weather Using An Ensemble of Neural Networks8 ConclusionAcknowledgmentsReferences
2. A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting
1 Introduction2 Model Selection3 Research Backround4 Literature Review5 Proposed Methodology5.1 Data Preparation5.2 MLPNN Implementation6 Results6.1 Preparation of Data6.2 Data Normalization Results6.3 Data Partition Results6.4 MLPNN Implementation Results7 Limitations8 Conclusion and Future DirectionsAcknowledgmentsReferences
3. Forecasting the Stock Market Index Using Artificial Intelligence Techniques
1 Introduction1.1 Motivation1.2 What is a Stock Market Index?1.3 Movement of the Stock Market Index1.4 Forecasting Approach1.5 Chapter Map2 Methodology3 Literature Review4 Forecasting Techniques5 Important Factors in Forecasting Stock Market Index6 Data Analysis6.1 Data Acquisitions6.2 Data Cleaning6.3 Feature Engineering6.4 Exploratory Data Analysis6.5 Model Building6.6 Error Metric7 ConclusionReferences
4. Forecasting of Environmental Sustainability through Green Innovation of E-Vehicle Industry
1 Introduction2 Variable Relevance2.1 Green Innovation2.2 Advancement and Social Cost2.3 Green Marketing and Its Evolution2.4 Consumer Citizenship Behavior3 Research Methodology3.1 Understandings4 Interpretations5 Discussion and Conclusion6 Further ResearchReferences
5. The Evolution of Forecasting Techniques Traditional Versus Machine Learning Methods
1 Introduction2 Evolution of Forecasting Techniques: An Overview2.1 Uses of Forecasting Techniques3 Traditional Forecasting Techniques3.1 Time Series Forecasting Techniques3.2 Regression Analysis4 Machine Learning Forecasting Techniques5 Accuracy Measures6 Differentiation Between Traditional and Machine Learning Forecasting Methods7 Advantages and Disadvantages of Traditional Forecasting and Machine Learning Forecasting Techniques7.1 Advantages of Traditional Forecasting Techniques7.2 Disadvantages of Traditional Forecasting Techniques7.3 Advantages of Machine Learning Forecasting Techniques7.4 Disadvantages of Machine Learning Forecasting Techniques8 Empirical Analysis8.1 Traditional Forecasting Techniques8.2 Machine Learning Forecasting Techniques9 ConclusionReferences

6. Workforce Forecasting after COVID-19 Pandemics Using Artificial Intelligence
1 Introduction2 Literature Review3 Artificial Intelligence and Machine Learning3.1 Classification3.2 Naïve Bayes3.3 Decision Tree4 Methodology 4.1 Python Implementation5 Observations and Result6 ConclusionReferences
7. Gender Disparity in Artificial Intelligence: Creating Awareness of Unconscious Bias
1 Introduction2 Research Methodology3 Results3.1 Understanding Gender bias3.2 Reasons behind Gender Bias in AI3.3 Strategies to Mitigate Gender Biases in AI4 Discussion5 Societal Implication6 ConclusionReferences
8. Customer Perspective through Artificial Intelligence Forecasting Green Products' Sustainable Development
1 Introduction1.1 The Theory of Planned Behavior1.2 Consumer Behavior Theory1.3 Artificial Intelligence and Customer Engagement 1.4 Citizenship Behavior and Consumer Satisfaction2 Literature Review3 Research Methodology4 Impactful Factors Influencing AI Forecasting5 ConclusionReferences
9. Weather Forecasting and Climate Behavioral Analysis Using Artificial Intelligence
1 Introduction2 Artificial Intelligence for Decision Support3 Artificial Intelligence in Weather Forecasting4 Artificial Intelligence in Climate Behavioral Analysis5 Case Study6 ConclusionAcknowledgmentsReferences
10. Assessing Climate Change through Artificial Intelligence: An Ethico-Legal Study
1 Introduction2 Climate Change—A Brief Introduction3 AI in the Contemporary World4 Application of AI4.1 Introduction4.2 AI and Weather Forecast4.3 AI in Mitigating Agricultural Emissions4.4 AI in Mitigating Electro Power Emission5 AI and Climate Change: Emerging Trends5.1 International Perspective5.2 National Perspective6 Ethico-Legal Challenges of AI in Climate Study1 Accuracy2 Automation Bias3 Data Privacy4 Sustainability/Sustainable measure/use5 Lack of transparency of AI tools6 Unemployment7 Justice8 Conclusion and SuggestionsReferences
11. Workforce Forecasting Using Artificial Intelligence
1 Introduction2 Workforce Forecasting2.1 Need for Workforce Forecasting2.2 Challenges in Workforce Forecasting3 Intelligent Techniques in Workforce Forecasting3.1 Machine Learning Algorithms3.2 Statistical Models4 Advantages and Disadvantages of AI in Forecasting4.1 Advantages of Workforce Forecasting with AI Techniques4.2 Disadvantages of Workforce Forecasting with AI Techniques5 Case Studies Based on the Implementation of AI for Workforce Forecasting6 ConclusionReferences
12. Artificial Intelligence-Based Approaches for Anticipating Financial Market Index Trends
1 Introduction1.1 Why is Machine Learning in Forecasting?2 Review of Available Methods2.1 Lstm2.2 Logistic Regression2.3 Linear Regression2.4 Random Forest2.5 Empirical Studies Using CNN2.6 Decision Trees2.7 RNN2.8 Bayesian Networks2.9 The Structure of CNN2.10 CNN vs Other Machine Learning Models2.11 Support Vector Machine (SVM)2.12 CNN and SVM3 Methodology3.1 Data Collection and Preprocessing3.2 Data Splitting3.3 Feature Extraction for CNN3.4 SVM Model Training3.5 Evaluation of the Model with Accuracy Measures3.6 Hyperparameter Tuning4 ConclusionReferences
13. A Comparative Analysis of Traditional and Machine Learning Forecasting Techniques
1 Introduction to Forecasting2 Types of Forecasting Techniques2.1 Qualitative Forecasting Techniques2.2 Quantitative Forecasting Techniques3 History of Forecasting4 Traditional Forecasting Techniques5 Machine Learning Oriented Forecasting6 Comparisons of Traditional And Machine Learning Approaches7 Current Trends in Forecasting8 Research Scope in Forecasting9 ConclusionReferences
14 Forecasting Demand for Paddy and Cotton in India Empirical Analysis Using Machine Learning Models
1 Introduction2 Basic Concept3 Background3.1 Predictive Analytics3.2 Planning Decisions3.3 Supply Response of a Crop3.4 Determining the Demand for a Crop3.5 Rice and Cotton Demand and Supply Scenario in India3.6 Machine Learning Techniques to Determine the Demand and Price of the Crop3.7 Nerlovian Expectation Model3.8 Price and Demand Forecasting of Rice and Cotton Using Deep Learning and Machine Learning Models3.8.2 Recurrent Neural Network (RNN) Model4 Model Architecture5 Application6 ConclusionReferences
15. Business Forecasting and Error Handling Using AI
1 Introduction1.1 Types of Business Forecasting1.2 Artificial Neural Networking1.3 Error Handling2 Business Forecasting2.1 Business Forecasting Using AI2.2 Disadvantages of not Using AI for Business Forecasting3 Error Handling3.1 Error Handling Using AI3.2 Error Handling Techniques4 ConclusionReferences
16. Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
1 Artificial Intelligence: An Introduction1.1 The Timeline1.2 Types of AI2 Global Pandemic and Mental Health2.1 Barriers to Accessing the Mental Health Services3 AI in Healthcare3.1 AI in the Mental Health System3.2 AI and Neurodevelopmental Disorders3.3 Brain-computer Interface (BCI)3.4 Brain Implants and AI3.5 The Super Clinician4 AI and Mental Health Therapy4.1 Wearable Technologies and Mental Therapy5 AI Chatbots: Transforming Mental Health Diagnosis5.1 Eliza5.2 Wysa: A Mental Health Chatbot for the World5.3 Dr. Bot5.4 Woebot5.5 Tess5.6 BlueSkeye AI5.7 Deliberate AI5.8 Upheal: For Mental Health Therapists5.9 Clare&me: Audio Bot6 AI and Mental Illness6.1 AI and the Language of Suicide7 AI Utilization in Mental Health7.1 Practical Benefits of AI in Mental Health7.2 Key Challenges of the Mental Health Support System8 Current AI Trends in Mental Health9 Future of AI in Mental Healthcare9.1 Will AI Replace Therapists?9.2 Pitfalls of Using AI in Mental HealthcareReferences
17. Predicting Stock Market Indexes with Artificial Intelligence
1 Introduction2 Artificial Intelligence2.1 Brief Description about AI2.2 AI and Its Principles3 Predicting the Stock Market with Artificial Intelligence3.1 Stock Prediction Concept3.2 Forecasting Stock Market by Machine Learning3.3 Stock Market Forecasting Using Intelligent Retrieval on Tick Data3.4 Support Vector Machines Use in Forecasting Stock Price Direction3.5 Prediction Accuracy4 The Application of Random Forests in Predicting Stock Prices and the Influence of Fiscal Performance and Trend Analysis on this Prediction5 Module Identification5.1 Captivating Data5.2 Training the Machine5.3 Data Scoring5.4 Experimental Results6 Future Scope7 ConclusionReferences
18. The Intellectual Structure of Application of Artificial Intelligence in Forecasting Methods: A Literature Review using Bibliometric Thematic Analysis
1 IntroductionEarly Years: 1860s–[1940s]Post-WWII: 1940s–[1970s]Oil Crises and Volatility: 1970s–[1990s]20.0 : Surging Prices and Financial Crisis20.0 : Shale Revolution and Price Collapse20.0 : COVID-19 Pandemic and Market RecoveryNeed for Study2 Bibliometric AnalysisMethodologyDatabase CurationIdentifying KeywordsThematic Analysis3 Fundamental Models3.1 Theoretical Analysis Model3.2 Structural Models4 Econometrics Models5 Artificial Intelligence Models6 Hybrid Models7 Comparative Analysis8 ConclusionReferences
19 Effective Temperature Prediction for An Enhanced Climate Forecast System
1 Introduction2 Methods2.1 Dataset3 Design/Prototype4 Results and Discussion5 ConclusionReferences
20. Demand Forecasting Methods: Using Machine Learning to Predict Future Sales
1 Introduction: Demand Forecasting1.1 Demand Planning: Understanding Market Needs1.2 Demand Sensing: Generating Interim Forecasts2 Literature Review3 Machine Learning3.1 Machine Learning for Demand Planning: Improved Precision at the Cost of Greater Intricacy3.2 Machine Learning Models Used in Sales Forecast4 Machine Learning Techniques and Demand Forecasting in Different Domains5 ConclusionReferences
21. Demand and Supply Forecasts for Supply Chain and Retail
1 Introduction1.1 Market Forces1.2 Forecasting1.3 Supply Chain1.4 Retail2 Need of Forecasting2.1 Need of Demand Forecasting2.2 Demand Forecasting Using ML3 Success in Retail31 Financial Statement Analysis32 Product Replenishment and BudgetingReferences
22. Role of Artificial Intelligence in Weather Forecasting and Climate Behavioral Analysis
1 Introduction1.1 Overview of Weather Forecasting and Climate Behavioral Analysis1.2 Issues in Conventional Weather Forecasting and Climate Behavioral Analysis1.3 Need for Artificial Intelligence in Weather Forecasting1.4 Organization of the Chapter2 Satellites in Weather Forecasting2.1 Satellite Remote Sensing2.2 Meteorological Satellites2.3 Impact of Satellite Data on Weather Prediction Models3 Review of AI-Based Weather Forecasting Systems3.1 ML-based Weather Forecasting Models3.2 DL-based Weather Forecasting Models3.3 Intelligent IoT Models for Weather Forecasting4 Comparison of Weather Forecasting Models4.1 Weather Databases4.2 Performance Measures4.3 Performance Comparison and Discussions5 Conclusions and Future Research ScopeReferences
Index
About the Editors

Content preview from Artificial Intelligence in Forecasting

Chapter 6 Workforce Forecasting after COVID-19 Pandemics Using Artificial Intelligence

Anjali Mathur

Associate Professor, VIT Bhopal University, Bhopal, Email: anjalimathur@vitbhopal.ac.in

1. Introduction

After the pandemic and lockdowns, most of the jobs turned into “work from home” mode. That creates two classes of employees: (1) one employee with multiple employers, and (2) job with entrepreneurship. It can be considered “Brain Drain” for the organizations or the job providers. Predictions of such employees or the forecasting the behavior of workforce become critical. In the new scenario, it does require to analyze the behavior of employees and do predictions on the basis of their behavior. Due to the uncertain behavior, the workforce can ...

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Artificial Intelligence in Forecasting

by Sachi Mohanty, Preethi Nanjundan, Tejaswini Kar

Chapter 6 Workforce Forecasting after COVID-19 Pandemics Using Artificial Intelligence

1. Introduction

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