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Choose the right trend model for the data

Choose from six trend models — linear, exponential, logarithmic, polynomial, power, and moving average — and compare them on any chart without restructuring underlying data.

Show trends with a linear trendline

A linear trendline shows the overall direction of data that increases or decreases at a consistent rate.

Show rapid growth with an exponential trendline

An exponential trendline models data that increases or decreases at an accelerating rate over time.

Show slowing growth with a logarithmic trendline

A logarithmic trendline models data that grows rapidly at first and then gradually slows over time.

Linear Trendlines

A linear trendline shows the overall direction of data that increases or decreases at a consistent rate. Use it when data shows steady growth or decline, since the line reduces the visual impact of short-term fluctuations.

Highlight steady growth or decline with a straight-line fit.
Reduce the impact of short-term fluctuations in the data.
Track long-term trends across any time-based series.
Combine with other series to compare multiple trends at once.

Charts with linear trendline showing steady growth.

Charts with exponential trendline.

Exponential Trendlines

An exponential trendline models data that increases or decreases at an accelerating rate over time. When growth or decline speeds up over time, it helps identify patterns where changes become progressively larger rather than remaining constant.

Model data that increases or decreases at an accelerating rate.
Identify growth patterns with progressively larger changes.
Analyze viral adoption, compound growth, and accelerating trends.
Compare exponential growth against linear baselines.

Logarithmic Trendlines

A logarithmic trendline models data that grows rapidly at first and then gradually slows over time. Use it when data shows early rapid growth followed by slower progress, since the line surfaces saturation patterns.

Model data with rapid early growth that gradually levels off.
Visualize diminishing returns and saturation patterns.
Highlight early gains versus long-term slowdown.
Compare against linear and exponential baselines.

Charts with logarithmic trendline.

Charts with polynomial trendline showing multiple peaks and valleys.

Polynomial Trendlines

A polynomial trendline models data with multiple changes in direction. Use it when data shows recurring peaks, valleys, or changing growth patterns, since the line surfaces underlying trends in non-linear datasets.

Model data with multiple changes in direction.
Visualize curves, fluctuations, and non-linear patterns.
Reveal underlying trends in datasets with repeated rises and declines.
Configure the polynomial order to fit complex data shapes.

Power Trendlines

A power trendline models relationships where one value changes proportionally to another. For correlated data that follows a non-linear relationship, it helps identify patterns between connected measurements and reveal how values increase or decrease relative to each other.

Model relationships where one value changes proportionally to another.
Reveal how values increase or decrease relative to each other.
Analyze correlated data with a non-linear relationship.
Use alongside linear and exponential trendlines for comparison.

Charts with power trendline showing proportional scaling.

Charts with moving average trendline smoothing volatile data.

Moving Average Trendlines

A moving average trendline smooths short-term fluctuations by averaging data points over a specified period. Use it when data shows frequent variations, since smoothing reduces visual noise and surfaces long-term direction.

Smooth short-term fluctuations by averaging over a specified period.
Reduce visual noise from temporary spikes or drops.
Highlight long-term trends in volatile or seasonal data.
Configure the moving average period to match the dataset.

GUIDED TRENDLINE SELECTION

Choose the right trendline in four steps

Developers can pick a trendline that matches the underlying data pattern, apply it to a chart, and compare multiple models without modifying the source dataset.

Analyze the data

Identify whether the data shows steady growth, acceleration, fluctuations, or leveling off.

Select a trendline

Choose linear, exponential, logarithmic, polynomial, power, or moving average.

Configure settings

Adjust forecasting, periods, or polynomial order as needed.

Compare results

Evaluate different trendlines and forecast future values.

PLATFORM SUPPORT

Add trendlines across platforms

Trendlines are available across web, desktop, and cross-platform frameworks. Availability of individual trendline types such as polynomial, power, and moving average may vary by platform.

Capability	JS, React, Angular, Vue	Blazor	ASP.NET Core & MVC	WPF & WinForms	.NET MAUI	Flutter
Linear trendline
Exponential trendline
Logarithmic trendline
Polynomial trendline
Power trendline
Moving average trendline
Forecast (forward and backward)

supported partially supported not supported

Frequently Asked Questions

✅ What is a Charts trendline?

A trendline is a line added to a chart that helps show the overall direction of the data. It reveals long-term direction by reducing the visual impact of short-term fluctuations.

✅ Can Charts display multiple trendlines on the same series?

Yes. Multiple trendlines can be added to the same series for comparison and analysis. This helps evaluate different trend models and identify the best fit for your data.

✅ When should I use an exponential trendline or a logarithmic trendline?

An exponential trendline is useful when data increases or decreases at an accelerating rate. A logarithmic trendline is ideal when changes are rapid initially and then gradually slow over time.

✅ Which chart types support Charts trendlines?

Trendlines can be added to line, scatter, column, area, candle, and hilo charts. They help highlight overall data patterns and support trend interpretation.

✅ Can Charts forecast future values using trendlines?

Yes. Trendlines can be extended beyond existing data points to estimate future values. They can also be extended backward to analyze and approximate past trends.

✅ When should I use a polynomial trendline?

A polynomial trendline is suitable for data that rises and falls multiple times. It helps represent complex patterns that cannot be accurately shown with a simple straight line.

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