
Explore time series forecasting as the art of predicting outcomes, measure forecasting accuracy, and see how forecasts drive decisions in sales, marketing, HR, and finance through a case study.
Explore time series fundamentals, including trend, seasonality (driven and calendar-driven), cyclic and irregular components, and learn to measure forecasting accuracy via train-test splits and percentage error metrics.
Identify whether a time series is seasonal by analyzing correlation with lagged sales data and determine a 12-month seasonality period using simple autocorrelation visuals.
Explore time series decomposition to break a series into trend, seasonality, and irregularity, and learn how business changes and consumer behavior shape these components.
Explore open source forecasting tools and methods, including arima/sarima/arimax, ets, transformers and lstms, ml like random forest and xgboost, and Prophet and TimeFM.
Decompose four years of monthly laptop sales in excel into trend, seasonality, and irregularity using multiplicative or additive methods, then forecast with trend, forecast, and averageif.
Watch how forecasting composes trend, seasonality, and irregularity back into a forecast using pre-composition, Excel's forecast function, and Excel Minor plugins, plus test and validation sets and error metrics.
Explore forecasting options in Power BI, from the built-in method and Dax to Python, R, Power Query, and Azure ML, with guidance on when to use each for enterprise forecasting.
Explore Power BI's inbuilt forecast function, which displays a blue historical monthly sales line alongside a yellow forecast line, with adjustable granularity, length, data exclusions, seasonality, and accuracy.
Explore how seasonality can be mistaken for granularity effects in pharmaceutical forecasting, illustrated by Remicade and Timpani, where weekly shipments create the 445 effect that undermines monthly forecasts.
Explore how weather driven seasonality shifts peaks from twelve to eleven months, impacting production, supply chain, and October sales, with calendar events like Diwali and Good Friday illustrating forecasting complexity.
Apply time series decomposition to reveal how trend, seasonality, and irregular components shape quarterly demand. Link changing consumer behavior and events like Black Friday to forecasting.
Learn why April's forecast is over indexed, causing 78% attainment despite weekly underperformance, and how setting realistic targets with a scientific forecasting approach guides business decisions.
Analyze spend versus sales seasonality indexes to optimize marketing dollars by reallocating from July and Black Friday to President's Day, boosting sales without extra spend.
Want to make smarter business decisions using data you already have? Learn forecasting the practical way — using Excel and Power BI!
This course is your hands-on guide to business forecasting, designed for non-technical professionals, students, analysts, and managers. Whether you're planning sales, budgeting, managing inventory, or forecasting customer demand, this course will show you how to do it using simple, accessible tools — no coding, no complex math.
You'll start by understanding core forecasting concepts — trends, seasonality, smoothing, and more. Then, using step-by-step demos in Excel and Power BI, you’ll learn how to create accurate forecasts, visualize them, and interpret the results.
But what really sets this course apart is our focus on the "so what?" — how to consume and apply forecasts in real business scenarios. You’ll explore 5 practical forecasting stories from different industries, and see how companies use forecasting to make confident decisions.
By the end, you’ll be equipped to:
Create and understand time series forecasts
Use Excel and Power BI to visualize and monitor trends
Apply forecast insights to real business problems
If you’re ready to turn data into foresight and action — enroll now and start forecasting with confidence!
All the best and have fun along the way!