How to Make a Time Series Plot in Power BI with AI

Tracking your business performance over time is fundamental, and a time series plot is the perfect tool for the job. This simple chart shows you how a key metric, like sales or website traffic, changes from one day, week, or month to the next. In this tutorial, we’ll walk through how to create a time series plot in Power BI, and then we'll show you how to let its built-in AI features do the heavy lifting to find trends and insights for you.

What is a Time Series Plot?

A time series plot (or time series graph) is a line chart that displays data points at successive, evenly spaced intervals over a specific period. One axis, usually the horizontal x-axis, represents time (days, months, years), and the other axis, the vertical y-axis, represents the value you’re measuring (revenue, users, clicks, etc.).

They are one of the most effective ways to visualize data because they instantly answer critical business questions like:

• Is our revenue growing over time? A clear upward trend is hard to miss.

• Are there seasonal patterns in our business? You might see sales spike every December or website traffic dip on weekends.

• Did that marketing campaign actually work? You can look for a significant lift in traffic or leads right after the launch date.

• Are there any weird outliers? An unexpected, dramatic spike or drop in your data can signal a problem or a huge opportunity worth investigating.

In short, these plots turn a boring spreadsheet of dates and numbers into a clear story about your business’s momentum and patterns.

Getting Your Data Ready for Analysis

Before you can build anything in Power BI, you need data that is properly structured. For a time series plot, the requirements are very straightforward. Your dataset must have at least two columns:

1. A Time Column: This is your date or timestamp information. It could be named Date, Order Date, Timestamp, or Month.

2. A Numeric Value Column: This is the metric you want to track over time. It could be named Sales, Revenue, Users, Sessions, or Ad Spend.

Your data might look something like this simple table from a Shopify export:

The most important step here is to make sure Power BI correctly identifies your time column as a date/time data type. When you load your data, Power BI usually does a good job of this automatically. If it doesn’t, you can easily fix it in the Power Query Editor by selecting the column and changing the “Data Type” in the ribbon. Clean, correctly formatted data is the foundation of any reliable report.

How to Make a Basic Time Series Plot in Power BI

Once your data is loaded into Power BI and ready to go, creating the basic line chart takes less than a minute. We'll use a standard Line chart visual for this.

Follow these quick steps:

1. Open the Report View: In Power BI Desktop, click on the "Report" icon on the left-hand navigation bar (it looks like a bar chart). This is the blank canvas where you build your visuals.

2. Select the Line Chart Visual: In the Visualizations pane on the right, find and click on the "Line chart" icon. A blank chart template will appear on your canvas.

3. Add Your Data: In the Data pane (far right), find your table. Click the arrow to expand it and see your columns. Now, simply drag and drop:

   • Drag your date column (e.g., Date) into the X-axis field in the Visualizations pane.

   • Drag your numeric column (e.g., Net Sales) into the Y-axis field.

Just like that, you have a time series plot! Power BI automatically plots your sales data over time. You’ll notice that Power BI creates a date hierarchy by default (Year, Quarter, Month, Day). You can use the drill-down and drill-up arrows on the visual's header to move between seeing your data from a high-level yearly view all the way down to a granular daily view.

Supercharge Your Plot with Power BI’s AI Features

Creating the basic chart is easy, but the real power comes from letting Power BI’s AI analyze the data for you. With just a few clicks, you can add forecasting and automatic anomaly detection without writing a single formula.

1. Forecasting Future Trends

Want to know where your sales or traffic are headed? The forecast feature uses your historical data to project future values, giving you a data-driven prediction of what to expect.

How to Add a Forecast:

1. Select Your Chart: Click on your newly created time series line chart to select it.

2. Open the Analytics Pane: In the Visualizations pane, click on the small magnifying glass icon to switch to the Analytics pane.

3. Add a Forecast Line: Scroll down and expand the Forecast section. Click the + Add button.

4. Configure the Forecast:

   • Forecast length: Define how far into the future you want to predict. For daily data, you might enter "30" for a 30-day forecast.

   • Confidence interval: This creates a shaded band around your forecast line, showing a range of likely outcomes. A 95% confidence interval (the default) means Power BI is 95% confident the actual value will fall within this range.

   • Seasonality: This is a crucial setting. It tells the forecasting model to look for repeating patterns. If you have daily data with weekly patterns (e.g., higher sales on weekends), you'd enter "7" for a 7-day seasonal cycle. You can leave it on "Auto-detect" and Power BI will try to find the pattern for you.

After clicking "Apply," your chart will now extend into the future, showing a dotted line for the predicted trend and a shaded area for the confidence band. This feature is unbelievably useful for goal setting, resource planning, and managing expectations.

2. Automatically Finding Anomalies

Anomalies are unexpected events in your data - sudden spikes or drops that don't fit the established pattern. Finding them manually is like trying to find a needle in a haystack, but Power BI’s AI can do it for you instantly.

How to Find Anomalies:

1. Select Your Chart: Once again, click on your time series plot.

2. Open the Analytics Pane: Navigate back to the magnifying glass icon.

3. Add Anomaly Detection: Scroll down to the Find anomalies section and click + Add.

4. Adjust Sensitivity: The main setting here is Sensitivity. This controls how sensitive the AI algorithm is when flagging outliers. A higher sensitivity will find more subtle deviations, while a lower setting will only flag the most significant events. Starting with the default (70%) is usually a good idea.

Once applied, Power BI will highlight any data points it considers anomalous with a circular marker. And here’s the best part: if you click on one of the anomaly markers, a new pane opens on the right, providing possible explanations. Power BI will analyze other fields in your data to suggest what might have caused the spike, saving you hours of investigative work.

The Easiest Method: Using Natural Language with Q&A

If dragging and dropping fields isn’t for you, Power BI has an even more intuitive AI feature: the Q&A visual. This allows you to create charts simply by asking questions in plain English.

Here’s how it works:

1. Double-click on any empty space on your report canvas, or select the "Q&A" visual from the Visualizations pane.

2. A text box will appear that says, “Ask a question about your data.”

3. Simply type what you want to see. For example:

   • "Show me total net sales by date as a line chart"

   • "Weekly users over the last year"

   • "Graph ad spend vs revenue per month"

As you type, Power BI’s AI interprets your request and instantly generates the appropriate visual. You don't have to know which fields to put on which axis - the AI handles it. Once the visual looks right, you can click the small icon on the top right to turn the Q&A result into a standard visual on your report, which you can then customize or add analytics features to, just like any other chart.

Final Thoughts

At the end of the day, a time series plot tells a story about your business’s journey. By leveraging Power BI’s accessible AI features like forecasting, anomaly detection, and natural language Q&A, you can uncover deeper insights without needing a degree in data science. These tools empower you to move beyond simply looking at data and start having a conversation with it.

Even with these AI-powered features, getting data connected and building reports in a tool like Power BI can be time-consuming. Here at Graphed, we’ve created a way to get these insights even faster by focusing entirely on the conversation. Instead of learning an interface, you simply connect your data sources (like Google Analytics, Shopify, Facebook Ads) and ask questions in natural language, like "Create a dashboard showing our daily Shopify sales and GA traffic for the last 90 days." Our platform automatically builds an entire live dashboard for you in seconds, no dragging-and-dropping required. If you're looking for the most direct path from question to insight, give Graphed a try.