Mastering Time Series Basics

2 Scenarios

2 Hours 30 Minutes

Intermediate

Popular

2 credits

Industry

retail-and-cpg

Skills

approach

problem-understanding

Tools

python

Learning Objectives

Understand what makes time series data unique and how temporal ordering affects forecasting accuracy.

Learn to decompose a time series into trend, seasonality, and residual components for clearer pattern recognition.

Grasp the concept of stationarity and explore methods like differencing and transformations to achieve it.

Explore lag correlation and autocorrelation functions (ACF) to identify dependencies between past and present data points.

Understand how moving averages and exponential smoothing reveal underlying trends and short-term variations.

Learn the structure and logic of ARIMA and SARIMA models for non-seasonal and seasonal forecasting.

Explore how ETS and Holt-Winters models capture error, trend, and seasonality components for robust predictions.

Understand forecast evaluation metrics like MAE, RMSE, and MAPE to measure and compare model performance effectively.

Overview

For many analysts, sales numbers are easy to plot but hard to predict. Time-based data behaves differently — each day depends on the one before, and ignoring that structure can make forecasts unreliable. This masterclass bridges that gap by teaching how to read, prepare, and model time series data to make confident predictions about future demand.

When time patterns are misunderstood, organizations overstock slow-moving products, under-supply fast sellers, and lose millions during key seasons. Understanding trend, seasonality, and autocorrelation is critical for building accurate models that directly impact logistics, marketing, and financial planning.

You’ll learn through interactive explanations, step-by-step visual examples, and hands-on snippets based on GlobalMart’s seasonal demand scenario. Knowledge checks and reflection questions throughout ensure you apply each concept before moving on to the next.

What You'll Learn:

Foundations of Time Series Analysis

Recognize what makes time-ordered data unique and why shuffling breaks its meaning
Decompose data into trend, seasonality, and residual components to reveal hidden patterns
Understand stationarity and how transformations or differencing stabilize a series for modeling

Correlation and Data Behavior

Use lag plots and autocorrelation functions (ACF) to detect dependencies between past and current values
Identify seasonal cycles and trends using visualization techniques

Forecasting Models in Action

Apply moving averages (SMA, EMA) to smooth fluctuations and highlight trends
Build forecasts with ARIMA and extend them using SARIMA for seasonal data
Explore Exponential Smoothing (ETS, Holt-Winters) to model both trend and seasonality effectively

Evaluating and Applying Forecasts

Measure forecast accuracy using MAE, RMSE, and MAPE
Connect statistical results to business outcomes like inventory planning and logistics optimization

By the end, you’ll understand how to turn raw time-based data into actionable forecasts — so you can anticipate demand, avoid stockouts, and improve campaign planning.
Test your knowledge throughout with scenario-based questions and interactive checks.

Prerequisites

Familiarity with writing Python code using libraries like pandas, NumPy, and matplotlib.
Understanding of what datasets, columns, and data types represent in structured data.
Awareness of basic statistical measures such as mean, variance, and correlation.
Knowledge of how to visualize data using line plots and interpret simple patterns over time.
General understanding of how machine learning models use input features to make predictions.