Introduction

Time series analysis plays an essential role in data science, enabling professionals to analyze data that is observed over time to uncover trends, seasonal patterns, and cyclical behaviors. This 5-day professional course provides a comprehensive introduction to the principles, methodologies, and practical applications involved in time series analysis. Participants will gain hands-on experience and learn how to apply various time series techniques to real-world datasets.

Objectives

• Understand the fundamental concepts of time series data and its components.
• Learn to visualize and explore time series data for insightful analysis.
• Master time series forecasting models to predict future trends.
• Gain proficiency in using statistical software for time series analysis.
• Apply time series techniques to real-world data science problems.

Course Outlines

Day 1: Introduction to Time Series Analysis

• Overview of time series analysis and its importance
• Types and components of time series data
• Time series data exploration and visualization
• Basic statistical concepts for time series
• Introduction to software and tools for time series analysis

Day 2: Time Series Decomposition and Smoothing Techniques

• Understanding time series decomposition: Trend, seasonal, and residual components
• Moving averages and exponential smoothing
• Decomposition methods: Additive and multiplicative
• Practical exercises on smoothing and decomposition techniques
• Case studies and real-world examples

Day 3: Time Series Models and Forecasting Techniques

• Introduction to time series forecasting
• Autoregressive (AR) and Moving Average (MA) models
• Autoregressive Integrated Moving Average (ARIMA) models
• Seasonal ARIMA (SARIMA) and ARIMAX models
• Hands-on forecasting exercises using software

Day 4: Advanced Time Series Modeling

• Introduction to state space models and Kalman filter
• Understanding GARCH models for volatility prediction
• Nonlinear models and machine learning approaches to time series
• Using R and Python for advanced time series modeling
• Interactive sessions with real-world datasets

Day 5: Practical Applications and Industry Use Cases

• Time series applications in finance, economics, and supply chain
• Project: Developing a time series model for a business problem
• Evaluating model performance and accuracy
• Presentation of project findings and peer review
• Course recap and Q&A session