Mastering Time Series Analysis and Forecasting with Python: Bridging Theory and Practice Through Insights, Techniques, and Tools for Effective Time Series Analysis in Python 9788196815103

Table of contents :
Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Technical Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Introduction to Time Series
Structure
Overview of Time Series
Applications of Time Series Across Industries
Usage in Finance and Economics
Stock Market Analysis
Market Risk Analysis
Credit Risk Analysis
Analysis of Economic Conditions
Usage in Sales and Marketing
Forecasting of Retail Sales
Seasonality Analysis and Planning
Inventory Management
Marketing Campaign Analysis
Customer Segmentation
Usage in Healthcare
Analysis of Patient Features in Treating Comorbidities
Disease Detection and Prediction
Usage in Weather and Environmental Science
Usage in Transportation and Traffic Management
Preparation of Time Series Data
Conclusion
References
2. Overview of Time Series Libraries in Python
Structure
Pandas for Time Series
NumPy for Time Series
Prophet for Time Series
AutoTS for Time Series
Conclusion
References
3. Visualization of Time Series Data
Structure
Introduction to Time Series Visualization Libraries of Python
Exploring Matplotlib and Its Uses
Exploring Seaborn and Its Uses
Exploring Plotly and Its Uses
Basic Time Series Plots with Matplotlib
Line Plot
Scatter Plot
Box Plot
Histogram
Advanced Time Series Visualization with Seaborn
Heat Map
Pair Plots
Interactive Time Series Visualization with Plotly
Area Plot
Candlestick Plot
Conclusion
References
4. Exploratory Analysis of Time Series Data
Structure
Loading and Inspection of Time Series Data
Understanding Descriptive Statistics
Mean
Median
Mode
Exploring Time Series Decomposition
Trend
Seasonality
Level
Noise
Performing Stationarity Analysis
Augmented Dickey-Fuller Test
Kwiatkowski–Phillips–Schmidt–Shin (KPSS) Test
Reviewing Autocorrelation and Partial Autocorrelation
Autocorrelation
Partial Autocorrelation
Exploring Rolling Statistics
Conclusion
References
5. Feature Engineering on Time Series
Structure
Univariate Feature Engineering
Creating Lag-Based Univariate Features
Calculating Rolling Statistics
Computing Expanding Window Statistics
Calculating Exponential Moving Averages
Multivariate Feature Engineering
Creating Lag-Based Multivariate Features
Creating Interaction Terms-Based Features
Creating Aggregated Features
Conclusion
References
6. Time Series Forecasting – ML Approach Part 1
Introduction
Structure
Data Introduction
Understanding Autoregressive Integrated Moving Average (ARIMA)
Model Documentation
Application of ARIMA on Time Series Data
Illustrating Exponential Smoothing Models
Model Documentation
Application of Simple Exponential Smoothing on Time Series Data
Double Exponential Smoothing
Application of Double Exponential Smoothing on Time Series Data
Triple Exponential Smoothing
Application of Triple Exponential Smoothing on Time Series Data
Exploring the Prophet Algorithm
Application of Prophet on Time Series Data
Conclusion
References
7. Time Series Forecasting – ML Approach Part 2
Introduction
Structure
Data Introduction
Applying Hidden Markov Models (HMM)
Application of HMM on Time Series Data
Understanding Gaussian Process
Application of Gaussian Process on Time Series Data
Developing a ML-Based Approach for Time Series Forecasting
Applying Support Vector Machine
Application of SVM on Time Series Data
Applying K-Nearest Neighbor (KNN)
Application of KNN on Time Series Data
Implementing with Random Forest
Application of Random Forest on Time Series Data
Implementing with Gradient Boosting
Application of Gradient Boosting on Time Series Data
Conclusion
References
8. Time Series Forecasting - DL Approach
Structure
Data Introduction
Understanding Long Short-Term Memory Networks
Developing a Deep Learning-Based Approach for Time Series Forecasting
Applying Gated Recurrent Units
Application of GRU on the time series data
Applying Convolutional Neural Networks
Application of CNN on the time series data
Conclusion
References
9. Multivariate Time Series, Metrics, and Validation
Structure
Data Introduction
Understanding Vector AutoRegression
Application of VAR on Time Series Data
Applying Vector Error Correction Model
Application of VECM on Time Series Data
Understanding VARMAX
Application of VARMAX on Time Series Data
Metrics for Time series
Conclusion
References
Index