Mastering Time Series Basics

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.

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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

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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.