Time Series Analysis in Power BI
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Build rolling, period-to-date and year-over-year calculations in Power BI, then forecast stock prices with built-in tools and DAX.
Time series analysis in Power BI looks at how sales, quantities and stock prices change over time, a dimension every dataset shares. You learn to recognize common patterns and date formats, reshape dates for analysis, and build window, period-to-date and period-over-period calculations on Superstore point-of-sale data across locations. Later work turns to Microsoft (MSFT) close prices, which you forecast and hindcast with the built-in forecasting feature and with DAX formulas based on growth rates.
What You Will Achieve
- Turn raw date fields into usable time measures, such as the delay before an order ships
- Build rolling averages and rolling totals of sales and quantity over a set period of time
- Produce a forecast with the built-in Power BI feature and test it by hindcasting past prices
- Project future values in DAX using CAGR and CMGR, and judge when CAGR is the right choice
Program Overview
Time Series Patterns and Transforming Dates
Introduces the patterns, formats and variation in time data, with practice on birth rate and influenza figures and on reshaping dates in a set order.
- Types of time series analysis
- Variation in birth rate and influenza data
- Order of operations for date changes
- Promotion effectiveness and receivables delay
Window Functions and Year Over Year Change
Builds period-based window calculations in Power BI and applies them to population, sales, review and discount data.
- Window functions and their applications
- Steps for a year over year comparison
- Five-star reviews and discount rate distribution
- Historical segment performance and customer ranking
Forecasting with Built-In Tools and DAX
Covers the basics of forecasting, first with the built-in Power BI feature and then with DAX formulas and forecasting variables.
- Forecasting order of operations
- Setting forecast expectations for energy consumption
- Microsoft close price data
- When CAGR applies, and CMGR
Certificate Awarded (Paid)
Job Outlook
Time-based calculations and forecasts in Power BI support business intelligence reporting, retail and sales analysis, and financial work that follows how prices and volumes move across periods.
Good for: Analysts building Power BI reports who need rolling calculations, period comparisons and simple forecasts on sales or stock price data.
Keep in mind: Forecasting is taught at a basic level, through the built-in Power BI forecast and DAX projections based on CAGR and CMGR, on Microsoft prices and energy data.
Bottom line: A focused Power BI course that pairs Superstore sales calculations with forecasts and hindcasts of Microsoft’s close price.
Frequently Asked Questions
What data do the Power BI reports work on?
Most of the work runs on Superstore point-of-sale data across locations and times, and on Microsoft (MSFT) close prices. Shorter exercises use birth rate, influenza, population, review, discount and energy consumption data.
How is forecasting handled in Power BI and in DAX?
The last chapter starts with the built-in forecasting feature, its order of operations and setting expectations, then forecasts and hindcasts the MSFT close price. It moves on to DAX, creating forecasting variables and projecting values with CAGR and CMGR.
What kinds of date problems does the first chapter tackle?
It works on identifying analysis types and variation in time data, transforming dates in the right order, calculating shipping and receivables delays, and analyzing how effective a promotion was over time.
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