How to Improve Forecast Accuracy Using Power BI?

Introduction

In the field of data science and data analytics, time series forecasting and time series analysis are important metrics in building data models or machine-learning models as it gives you the ability to “see” ahead of time and make plans in your business accordingly. One tool that can help with this is Power BI – a business analytics service, Power BI data Accuracy that provides interactive visualizations, formatting, and business intelligence capabilities, Power BI Forecasting.

Time series forecasting is important for data scientists as they use different tools like excel, power bi, different data models, and forecasting models for data analysis. In this blog, we will look at what time series forecasting is, how Microsoft Power BI makes time series forecasting graphs, line chart visuals for forecasting, DAX, power query editor, and modules that power bi uses for forecasting.

Learning Objectives

• This tutorial will teach you about time series forecasting in power bi.
• You will get acquainted with the different types of time series and their use cases.
• Also, the step-by-step guide will teach you how to create time series forecasting graphs in power bi.

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, etc.

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.

Stationary Time Series and Non-Stationary Time Series

Stationary Time Series

Stationary time series is a time series in which Statistical properties such as mean, variance, and other statistical properties remain constant over time. In other words, stationary time series is a series whose statistical properties remain independent at any observed point in time. A stationary time series has a constant variance.

Non-Stationary Time Series

Stationary time series is a time series in which Statistical properties such as mean, variance, and other statistical properties change with time. In another sense, we can say stationary time series has a trend, while a non-stationary time series does not have a constant variance.

Time Series Forecasting in Power BI

Power BI has several built-in features that can help with time series forecasting. These features include time intelligence, forecasting, and custom visualizations.

The time intelligence feature in Power BI allows users to create custom time periods, such as weeks or months, and aggregate data according to those periods. This can help with analyzing data over time and identifying trends and patterns.

The forecasting feature in Power BI allows users to create forecasts based on historical data. Users can specify the number of periods to forecast and the algorithm to use. The forecasting feature also provides visualizations that show the accuracy of the forecast.

Custom visualizations in Power BI can also be used for time series forecasting. Users can create custom visualizations that display historical data and forecasted values. These visualizations can be highly customized and can include features such as confidence intervals and trend lines.

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). Additive trend Power BI uses the appropriate model automatically when you start a forecast for your sales data line chart based on an analysis of the historical data.

Steps for Time Series Forecasting in Power BI

1. Import historical data from excel, csv, SQL, and many more data sources into Power BI. This data should include a time dimension, such as dates or months, and a metric, such as sales or revenue.
2. Create a time intelligence calculation. This calculation should aggregate the data according to a time period, such as weeks or months.
3. Use the forecasting feature to create a forecast based on the historical data. Specify the number of periods to forecast and the algorithm to use.
4. Create custom visualizations that display the historical data and forecasted values. These visualizations should include features such as trend lines and confidence intervals.

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, Power BI Forecast Accuracy.

Creating the line graph :

For forecasting, go to the analytics pane and find the Forecast option. Now, 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 is our confidence interval.

Conclusion

If you want to see quickly the trend and forecasting in the same frame to understand and make any business decision, Power BI can help you. You can use Arima and other time series modules in Python or R. In conclusion, Power BI provides several useful features for time series analysis, making power bi a powerful tool for visualizing and Power BI Forecast Accuracy.

Key Takeaways

• Time series forecasting is a technique in machine learning which analyzes data and the sequence of time to predict future events. We use power BI to implement time series.
• This technique provides near-accurate assumptions about future trends based on historical time-series data.
• Time series can be either stationary or non-stationary.

Frequently Asked Questions