Dive deep into the world of time series data with this comprehensive guide that illuminates the path to understanding an
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English Pages 247 Year 2024
Table of contents :
Book 1 - Time Series Data Analysis: A Comprehensive Guide for Very Beginner
Introduction to Time Series Analysis
What is Time Series Data?
Importance and Applications
Challenges in Time Series Analysis
Fundamental Concepts in Time Series
Components of Time Series Data
Stationarity and Seasonality
Autocorrelation and Partial Autocorrelation
Data Preparation and Cleaning for Time Series
Handling Missing Values
Data Transformation and Normalization
Seasonal Adjustment Techniques
Exploratory Data Analysis (EDA) for Time Series
Visualizing Time Series Data
Descriptive Statistics in Time Series
Identifying Patterns and Anomalies
Time Series Forecasting Methods
Simple Moving Average (SMA) and Exponential Smoothing
ARIMA and Seasonal ARIMA
Advanced Methods: SARIMAX, VAR, and State Space Models
Machine Learning in Time Series Analysis
Feature Engineering for Time Series
Regression Models for Time Series Forecasting
Tree-based Methods and Ensemble Learning
Deep Learning for Time Series Forecasting
Introduction to Neural Networks
Recurrent Neural Networks (RNNs) and LSTM
Convolutional Neural Networks (CNNs) for Time Series
Transformer Models and Time Series
Evaluating Forecasting Models
Error Metrics for Time Series
Cross-Validation Techniques in Time Series
Model Selection and Optimization
Case Studies and Applications
Financial Market Analysis
Weather Forecasting
Energy Demand Forecasting
Retail Sales and Inventory Management
Advanced Topics in Time Series Analysis
Multivariate Time Series Analysis
Time Series Clustering and Classification
Anomaly Detection in Time Series
Real-Time Time Series Analysis
Tools and Software for Time Series Analysis
Python and R for Time Series Analysis
Time Series Databases: InfluxDB, TimescaleDB
Visualization Tools: Grafana, Tableau
Best Practices and Pitfalls in Time Series Analysis
Data Management and Scalability
Interpretability of Models
Ethical Considerations and Bias
Future Directions in Time Series Analysis
Integrating Time Series with Big Data and IoT
Advances in Machine Learning and AI for Time Series
The Role of Time Series in Digital Twins and Simulation
Book 2 - Time Series Data Analysis: A Comprehensive Guide for Very Beginner
Introduction to Time Series Analysis
What is Time Series Analysis?
Applications of Time Series Analysis
Getting Started with R
Installing and Setting Up R and RStudio
Basic R Programming Concepts
Introduction to R Packages for Time Series Analysis
Time Series Data in R
Understanding Time Series Data
Importing Time Series Data into R
Time Series Data Objects in R (ts, xts, zoo)
Visualizing Time Series Data
Time Series Decomposition
Trend, Seasonality, and Noise
Decomposing Time Series Data
Decomposition Methods in R
Stationarity and Differencing
What is Stationarity?
Testing for Stationarity (ADF Test)
Achieving Stationarity through Differencing
Autoregressive Integrated Moving Average (ARIMA) Models
Understanding ARIMA Models
Fitting ARIMA Models in R
Model Diagnostics and Validation
Forecasting with ARIMA Models
Seasonal ARIMA and SARIMA Models
Extending ARIMA to SARIMA
Fitting SARIMA Models in R
SARIMA Model Diagnostics and Forecasting
Advanced Time Series Models
Exponential Smoothing State Space Models
Vector Autoregression (VAR) Models
Dynamic Time Warping (DTW)
Machine Learning for Time Series Analysis
Time Series Cross-Validation
Understanding Time Series Cross-Validation
Implementing Time Series Cross-Validation in R
Case Studies
Financial Market Analysis
Weather Forecasting
Demand Forecasting in Retail
Anomaly Detection in Web Traffic Data
Best Practices and Tips for Time Series Analysis
Data Preparation and Cleaning
Choosing the Right Model
Model Interpretation and Reporting
Future Directions in Time Series Analysis
Big Data and Time Series Analysis
Integrating Machine Learning and Deep Learning
Time Series Analysis in the Era of Cloud Computing