TIME SERIES AND FORECASTING

INTRODUCTION

Time series analysis is a critical tool for identifying patterns, trends, and seasonal variations in data, allowing for accurate forecasting and predictive modeling. Whether applied in business, finance, healthcare, or manufacturing, time series techniques help organizations make informed decisions based on historical data.

This one-day course provides a structured introduction to time series components, trend analysis, smoothing techniques, and advanced forecasting methods. Participants will learn how to identify different elements of time series data, apply exponential smoothing techniques, and understand ARIMA models for forecasting.

Additionally, the course will introduce BATS and TBATS models within R, providing an overview of their capabilities in handling complex seasonality.

This John Varlow | Training and Consultancy course will cover:

✔ Understanding time series data and its relationship with process control and regression models
✔ Identifying secular trends, cyclical variation, seasonal variation, and residual variation
✔ Differentiating between linear and non-linear trends
✔ Knowing when and how to transform data for better model accuracy
✔ Applying rolling averages, weighted data, and smoothing techniques
✔ Implementing exponential smoothing methods, including seasonal adjustments
✔ Understanding the difference between additive and multiplicative models
✔ Recognizing the importance of stationarity in ARIMA models and applying transformations
✔ Interpreting ARIMA model outputs, including auto-correlation and partial auto-correlation functions
✔ Gaining an overview of BATS and TBATS models within R for advanced forecasting

COURSE OBJECTIVES

By the end of this one-day course, participants will:

✔ Understand time series data structures and how they differ from other data types
✔ Recognize different sources of variation in time series data
✔ Identify trends, seasonality, and cyclical effects in data
✔ Learn when and how to apply transformations to improve model performance
✔ Use rolling averages, smoothing, and exponential smoothing techniques for forecasting
✔ Distinguish between additive and multiplicative forecasting models
✔ Gain an understanding of stationarity and its role in ARIMA modeling
✔ Learn how to interpret ARIMA model outputs using auto-correlation techniques
✔ Get an introduction to BATS and TBATS models in R for handling multiple seasonal patterns

TRAINING METHODOLOGY

This session is interactive and application-focused, blending theoretical learning with hands-on forecasting exercises. Participants will:

✔ Work with real-world time series datasets
✔ Use Excel and statistical tools to apply forecasting methods
✔ Interpret model outputs and discuss practical forecasting challenges
✔ Engage in guided discussions on best practices for time series analysis

By the end of the session, participants will have practical experience in analyzing time series data and applying forecasting techniques effectively.

WHO SHOULD ATTEND?

This course is designed for professionals who need to analyze time-related data, detect patterns, and develop accurate forecasts. It is particularly relevant for:

✔ Business & Data Analysts forecasting trends in business performance
✔ Operations & Supply Chain Professionals managing demand and capacity planning
✔ Healthcare & Epidemiology Specialists tracking patient or disease trends
✔ Finance & Risk Analysts modeling economic or financial time series
✔ Manufacturing & Engineering Teams improving process forecasting

Pre-requisites:

✔ Basic numeracy skills
✔ Completion of "Introduction to Descriptive Statistics" or equivalent understanding
✔ Completion of "Introduction to Inferential Statistics" or equivalent understanding

COURSE OUTLINE

1. Introduction to Time Series Analysis

✔ Defining time series data and understanding its importance
✔ How time series differs from cross-sectional and panel data
✔ The relationship between time series, process control, and regression models

2. Identifying Trends & Seasonality

✔ Understanding secular trends, cyclical variation, seasonal variation, and residual variation
✔ Identifying linear vs. non-linear trends
✔ Knowing when to apply data transformations to improve accuracy

3. Smoothing Techniques & Exponential Smoothing

✔ Introduction to rolling averages and weighted moving averages
✔ Understanding simple, double, and triple exponential smoothing
✔ Implementing seasonal exponential smoothing models
✔ Differentiating between additive and multiplicative models

4. Introduction to ARIMA Modeling

✔ Understanding stationarity and the need for differencing
✔ Identifying autocorrelation and partial autocorrelation functions (ACF & PACF)
✔ How to interpret ARIMA model outputs for forecasting

5. Overview of Advanced Forecasting Models: BATS & TBATS

✔ Introduction to BATS and TBATS models within R
✔ Understanding when to use BATS and TBATS for handling complex seasonality

6. Practical Applications & Case Studies

✔ Applying time series forecasting techniques to real-world datasets
✔ Understanding forecast limitations and interpreting confidence intervals
✔ Addressing practical issues in time series forecasting

DURATION

⏳ 1 Day

This course provides a structured and practical introduction to time series forecasting, equipping participants with the skills to apply time series techniques confidently using statistical tools and Excel.

CERTIFICATION

Upon successful completion of this course, participants will receive a John Varlow | Training and Consultancy Certificate of Completion.