AN INTRODUCTION TO TIME SERIES ANALYSIS

INTRODUCTION

Time series analysis is essential for understanding patterns, identifying trends, and making reliable forecasts based on historical data. In business, healthcare, finance, and operations, accurately predicting future activity can lead to better decision-making, resource planning, and risk management.

This one-day course introduces participants to the fundamentals of time series analysis and forecasting techniques, with a particular focus on practical applications using Microsoft Excel. Participants will learn how to set up and apply simple forecasting methods, use Excel’s FORECAST and GROWTH functions, and explore exponential smoothing models (ETS) for improved forecasting accuracy.

The session will emphasize real-world applications and provide hands-on experience in identifying trends, managing seasonality, and interpreting forecast limitations.

This John Varlow | Training and Consultancy course will cover:

✔ Key considerations before applying time series methods
✔ Setting up and using simple forecasting techniques
✔ Confidently using FORECAST and GROWTH functions in MS Excel
✔ Recognizing the limitations of Excel’s built-in forecasting functions
✔ Identifying trends and seasonality in data
✔ Using regression functions in Excel for seasonal analysis
✔ Understanding the advantages of exponential smoothing models over regression models
✔ Choosing the right type of exponential smoothing (additive vs. multiplicative models)
✔ Setting up and using exponential smoothing spreadsheets
✔ Understanding the limitations of forecasts, including confidence intervals
✔ Recognizing practical challenges with time series data

COURSE OBJECTIVES

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

✔ Understand the key considerations before applying time series models
✔ Learn how to set up and use simple forecasting techniques in Excel
✔ Gain confidence in using FORECAST and GROWTH functions in MS Excel
✔ Recognize trends and seasonal patterns in time series data
✔ Apply regression techniques in Excel for analyzing seasonal effects
✔ Understand why exponential smoothing models outperform simple regression in certain scenarios
✔ Identify the correct exponential smoothing method for different data types
✔ Set up and apply ETS models for practical forecasting
✔ Interpret forecast accuracy, limitations, and confidence intervals
✔ Address real-world challenges in time series forecasting

TRAINING METHODOLOGY

This session is interactive and application-focused, combining theoretical learning with hands-on Excel-based exercises. Participants will:

✔ Work with real-world time series datasets
✔ Practice forecasting techniques using MS Excel
✔ Explore different smoothing models through step-by-step examples
✔ Discuss challenges and solutions in time series forecasting

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 forecasts. It is particularly relevant for:

✔ Business & Data Analysts needing to predict trends
✔ Operations & Supply Chain Professionals managing demand forecasting
✔ Healthcare & Epidemiology Specialists analyzing patient or disease trends
✔ Finance & Risk Analysts modeling economic or financial time series
✔ Manufacturing & Engineering Teams improving process forecasting

Pre-requisites:

✔ Basic numeracy skills
✔ Familiarity with Microsoft Excel is recommended

COURSE OUTLINE

1. Foundations of Time Series Analysis

✔ What is time series data, and why is it important?
✔ Key assumptions and challenges in analyzing time-related data
✔ Understanding different types of variation in time series

2. Introduction to Forecasting in Excel

✔ Setting up and using basic forecasting techniques
✔ Applying the FORECAST and GROWTH functions in Excel
✔ Recognizing the limitations of simple forecasting methods

3. Identifying Trends & Seasonality

✔ Understanding linear vs. non-linear trends
✔ Identifying seasonality and cyclical patterns in data
✔ Using Excel regression functions for seasonal analysis

4. Introduction to Exponential Smoothing (ETS) Models

✔ Why use exponential smoothing instead of regression?
✔ Choosing between additive vs. multiplicative models
✔ Understanding single, double, and triple exponential smoothing

5. Applying Exponential Smoothing in Excel

✔ Setting up an exponential smoothing spreadsheet
✔ Interpreting ETS model outputs
✔ Understanding forecast accuracy and confidence intervals

6. Addressing Practical Issues in Forecasting

✔ Recognizing common data issues in time series forecasting
✔ Understanding forecast uncertainty and limitations
✔ Strategies for improving forecast reliability

DURATION

⏳ 1 Day

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

CERTIFICATION

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