Introduction Photo by Aron Visuals on Unsplash Time series forecasting is a really important area of Machine Learning as it gives you the ability to “see” ahead of time and make plans in your business accordingly.
In this blog, we will look at what time series forecasting is, how Power BI make time series forecasting graphs and modules that power bi uses for forecasting.
What Is Time Series Forecasting? Time series is the collection of data at regular intervals in terms of Days, Hours, Months, and Years.
Time series forecasting is a technique in machine learning, which analyzes data and the sequence of time to predict future events.
This technique provides near accurate assumptions about future trends based on historical time-series data.
Time series allows you to analyze major patterns such as trends, seasonality, cyclicity, and irregularity.
It is used for various applications such as stock market analysis, pattern recognition, earthquake prediction, economic forecasting, census analysis, and so on.
Time series includes trend cycles and seasonality.
Unfortunately, many confuse seasonal behavior with cyclic behavior.
To avoid confusion, let’s understand what they are: Trend: An increase or decrease in data over a period of time is called a trend.
Seasonal: Oftentimes, seasonality is of a fixed and known frequency.
For example, seasonal factors like the time of the year or the day of the week, a seasonal pattern occurs.
Cyclic: When a data exhibit fluctuates, a cycle occurs.
But unlike seasonal, it is not of a fixed frequency.
Which algorithm does Power View use for time series forecasting? Power BI provides two versions of exponential smoothing, one for seasonal data (ETS AAA), and one for non-seasonal data (ETS AAN) Power BI uses the appropriate model automatically when you start a forecast for your line chart, based on an analysis of the historical data.
How To create time series forecasting charts in Power BI.
In this tutorial, I am using the below dataset.
To use the forecasting feature we use the Analytics tab, The Analytics pane allows you to add dynamic reference lines to your visuals to provide a focus for important trends or insights.
It is found in the Visualizations area of Power BI Desktop.
Creating the line graph : For Forecasting, go to the analytics pane we can see a Forecast option.
Let’s click on Add, set the forecast length to 6 Years with a 95% Confidence Interval, and click Apply.
You’ll notice now that we have a forecast line after our data ends and the shaded grey area being our confidence interval.
Conclusion If you want to see quickly the trend and forecasting in the same frame to understand and to make any business decision, Power BI can help you.
You can use Arima and other time series modules as well in Python or R, next time I will talk about Arima with Python.
I hope this article will help you and save a good amount of time.
Let me know if you have any suggestions.
About the Author Prabhat Pathak – Associate Analyst I am an engineer currently working in Top MNCs as an Associate Analyst and Innovation Enthusiast.
I love learning new things, I Believe Every data has a story and I love reading the stories.
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