Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session

Course Overview:

This one-day introductory course gives you a practical foundation in time-series analysis, enabling you to understand, interpret, and forecast trends using sequential data. You will learn essential concepts such as patterns, seasonality, trend behaviour, and basic forecasting techniques used across analytics, finance, and business decision-making. Through hands-on exercises, you will gain clarity on how time-series data behaves and how to apply simple forecasting models to real datasets. By the end of the course, you will be confident in analysing time-based data and using it to support smarter decisions.

Learning Objectives:

By the end of the course, you will:

• Understand time-series basics and key components like trend and seasonality.
• Analyse and interpret sequential data with clarity.
• Use moving averages and smoothing techniques effectively.
• Apply simple forecasting methods suitable for beginners.
• Recognize model patterns and their business impact.
• Prepare data for time-series analysis with essential techniques.
• Use visualisations to detect patterns, anomalies, and shifts in data.

Target Audience:

Data analysts, statisticians, and finance professionals.

Why is it the Right Fit for You?

This course is ideal if you want to understand how time-based data behaves and how to use it for forecasting and decision-making. It focuses on practical, beginner-friendly methods that can be immediately applied in real-world analytics and business contexts. You will gain clarity through step-by-step explanations and hands-on exercises designed to build confidence. Our approach ensures that you not only learn the techniques but also understand how and when to use them effectively. By the end, you will be able to interpret trends and make informed predictions using time-series concepts.

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Agenda

Module 1: Introduction to Time Series

• What time-series data is and where it is used
• Key terms and concepts
• Difference between cross-sectional and time-based datasets
• Activity

Module 2: Components of Time Series

• Trend, seasonality, and cyclic behaviour
• Irregular and random variations
• How patterns affect forecasting outcomes
• Activity

Module 3: Time-Series Visualization & Interpretation

• Plotting line charts and trend visuals
• Identifying key behaviours through graphs
• Detecting anomalies and outliers
• Activity

Module 4: Moving Averages & Smoothing Techniques

• Simple and weighted moving averages
• Smoothing noisy data
• When to use smoothing vs. direct forecasts
• Activity

Module 5: Basic Forecasting Concepts

• Introduction to naive forecasting
• Understanding lag and lead values
• Short-term vs long-term forecasting principles
• Activity

Module 6: Time-Series Models Overview

• Basics of ARIMA concepts (at a beginner level)
• Using autocorrelation ideas
• Model assumptions and time dependence
• Activity

Module 7: Data Preparation for Time-Series Analysis

• Handling missing values
• Resampling and frequency adjustments
• Transformations for stability
• Activity

Module 8: Real-World Applications & Case Insight

• Using time-series in finance, retail, operations, and analytics
• Common mistakes to avoid
• Practical interpretation tips
• Case Study