timeline

100%

timeline - correlation

✓ Published0🌍 Public

Last edited Dec 3, 2018

Created on Jan 26, 2016

This example demonstrates how to assess the correlation between two time series. It shows a line chart for each series and a scatter plot of one series against the other, where the alignment of points indicates the strength of correlation. A dynamic coefficient of correlation is displayed, computed using the least squares method. Users can interactively drag data points, adjust trends, and modify dispersion to see their impact, with d3.svg transitions animating the changes.

AI-generated description

An example of how to decide if 2 time series are correlated or not.

Graphically speaking, one can estimate the correlation of 2 time series by using a scatter plot of those 2 time series (right top graph): the more the points are aligned in a straight line, the more the time series are correlated.

Computationnaly speaking, the coefficient of correlation gives an insight of the dispersion of those points: the more the coefficient of correlation is near 1 (or -1), the more the scatter points are aligned, and the more the 2 time series are correlated.

Usages :

in the left graph, Drag & Drop a point to see the impact on the coefficient of correlation
in the left graph, Drag & Drop a timeline (to change each values of the related time serie), and see that it has no impact on the coefficient of correlation;
in the left graph, correlate or decorrelate time serie 2 and time serie 1 by introducing some random values; then see the impact on the coefficient of correlation;
in the left graph, increase or decrease the trend of the second time serie, and see that it has no impact on the coefficient of correlation;
in the left graph, inverse a trend, and see that it inverses the coefficient of correlation; when the 2 trends increases/decreases in the same way, the coefficient of correlation is positive; at the opposite, when one trend is increasing and the other is decreasing, the coefficient of correlation is negative;
in the left graph, disperse/concentrate the second time serie, and see that the more disperse is the second timeserie, the less it correlates with the first time serie;

Notes:

trend line computed using least square method

Acknowledgments:

done with D3 v3.5.5
<a href='http://blockbuilder.org'>blockbuilder.org</a>

mit Licensed

Similar vizzes

Loading thumbnail…

timeline - comparing trends

This example shows how to compare trends between two time series by presenting raw and relative (mean-reduced) data side-by-side. The left graph displays raw values, while the right graph shows each value divided by its series mean, allowing users to assess whether absolute or proportional changes are more meaningful. Drag-and-drop interactions enable manipulation of individual data points or entire timelines, with the linked reduced trends updating in real-time. Built with D3 v3.5.5, the visualization uses SVG for rendering, d3.behavior.drag for interactions, and a least-squares regression line computed for each trend.

KKcnarf

75% match

Loading thumbnail…

timeline - seasonality detection (II)

This interactive D3.v3 visualization demonstrates how to detect seasonality in a time series using a correlogram, showing both the timeline and its autocorrelation coefficients. Users can drag data points to modify the timeline, adjust season length, seasonality magnitude, and trend via controls. The left panel displays the timeline with draggable dots, while the right panel shows correlation bars for lags 1-10. The example reads from a CSV file and uses d3.behavior.drag and d3.svg.axis to enable manipulation and rendering.

KKcnarf

74% match

Loading thumbnail…

timeline - trend

The visualization demonstrates how a single outlier or a user-dragged point dramatically alters a least-squares trend line. It shows a scatter plot of a 20-point time series, where each blue dot can be moved with the mouse to immediately recalculate and redraw the steel-blue regression line. A timeline path connects the points, and buttons allow inverting the trend or creating an outlier. Built with D3.v3, it uses `d3.behavior.drag`, `d3.svg.axis`, and `transition` functions to animate updates, with data loaded from a CSV via `d3.csv`.

KKcnarf

74% match

Loading thumbnail…

timeline - seasonality detection (I)

This interactive visualization tests a time series for seasonality by comparing it against a shifted version of itself. The left panel displays the original series and its lagged copy as draggable points, while the right panel plots the correlation coefficient for different shift amounts. Users can adjust the suspected season length and the magnitude of the seasonal component. The D3 v3 code computes autocorrelation values, visually linking the lagged timeline, the correlation plot, and a bar chart. Data comes from a CSV file and is rendered as SVG with animated transitions.

KKcnarf

72% match

Loading thumbnail…

timeline - trend & seasonality

An interactive D3.v3 timeline visualizes the impact of seasonality on a global trend. Users can drag individual data points, invert, or permute the seasonality to explore how internal seasonal trends affect the overall slope. The chart displays raw values, deseasonalized season means (large dots), and a least-squares trend line, all within an SVG with animation. Dragging points updates the trend in real time, demonstrating how a windowed-mean approach mitigates seasonal effects.

KKcnarf

70% match

Loading thumbnail…

timeline - seasonality detection (III)

This interactive D3 v3 visualization demonstrates why detrending a time series before computing a correlogram is essential for detecting seasonality. It shows a synthetic timeline with adjustable season length, seasonality magnitude, and overall trend, alongside a correlation bar chart for each lag. Users can toggle detrending; with the raw series, strong trends mask seasonal patterns, while detrended data reveals even small seasonal cycles clearly. The code uses D3’s linear scales, SVG rendering, drag behavior, and transition animations, with data generated inline and links to adjust parameters.

KKcnarf

70% match

Loading thumbnail…

This example displays a scatterplot of 50 randomly generated data points, with circles positioned on an SVG canvas using x and y scales. The visualization includes formatted percentage axes on both the bottom and left sides, while circle radius encodes the y-value through a separate linear scale. The code uses d3.scale.linear for coordinate mapping, d3.svg.axis for axis generation with tick formatting via d3.format, and d3.select to append SVG elements, demonstrating a simple D3 v7 workflow with a dynamically created dataset.

MManjunath

69% match

Loading thumbnail…

timeline - trend, confidence interval, outliers

This interactive example demonstrates how a trend line and its 95% confidence interval respond to changes in time-series data. Users can drag individual data points to see their immediate impact on both the trend and the confidence interval, or drag the entire timeline to show that time-axis shifts have no effect. The visualization also provides controls to increase or decrease data dispersion, which alters the confidence interval but not the trend, and to force an outlier. The trend is computed using the least squares method, with the confidence interval based on ±1.96 standard deviations. Built with D3 v3.5.5, the SVG rendering uses `d3.behavior.drag` for interactions and transitions for smooth animations.

KKcnarf

69% match