diff --git a/demo/helpers.js b/demo/helpers.js index eba3858..c2a9151 100644 --- a/demo/helpers.js +++ b/demo/helpers.js @@ -1,6 +1,22 @@ export function getSize() { + const margin = window.innerWidth <= 768 ? 30 : 100; + const BREAKPOINTS = { + MOBILE: 480, + TABLET: 768 + }; + + const DENOMINATORS = { + MOBILE: 2.5, // Larger height ratio for mobile + TABLET: 3, // Medium height ratio for tablet + DESKTOP: 4 // Smaller height ratio for desktop + }; + + const denominator = + window.innerWidth <= BREAKPOINTS.MOBILE ? DENOMINATORS.MOBILE : + window.innerWidth <= BREAKPOINTS.TABLET ? DENOMINATORS.TABLET : + DENOMINATORS.DESKTOP; return { - width: Math.min(window.innerWidth - 100, 1100), - height: window.innerHeight / 4, + width: Math.min(window.innerWidth - margin, 1100), + height: window.innerHeight / denominator, }; } diff --git a/demo/index.html b/demo/index.html index 112d635..6b36522 100644 --- a/demo/index.html +++ b/demo/index.html @@ -1,11 +1,10 @@ - - - - - augurs demo - - - - - - - -

augurs demo

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- augurs is a time series analysis library for Rust with bindings for JavaScript. It provides a set of tools for - analyzing time series data, including clustering, outlier detection, forecasting, and changepoint detection. -

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- Visit the documentation for more information. - Alternatively, give augurs a star on GitHub! -

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- Clustering with DBSCAN - calculating... -

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- augurs can be used to identify groups of series that behave similarly through time. This can be slow for large - number of series but can be parallelized! -

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augurs demo

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+

+ augurs is a time series analysis library for Rust with bindings + for JavaScript. It provides a set of tools for analyzing time + series data, including clustering, outlier detection, + forecasting, and changepoint detection. +

+

+ Visit the documentation for + more information. Alternatively, give augurs a star on + GitHub! +

+
+
+ +
+

+ Clustering with DBSCAN - calculating... +

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+ augurs can be used to identify groups of series that behave + similarly through time. This can be slow for large number of + series but can be parallelized! +

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+ + + +
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- Outlier detection with DBSCAN - calculating... -

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- A similar but easier problem is to identify series that are outliers: those which behave differently to the - majority of series. There are several algorithms; this demo shows the DBSCAN algorithm. -

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Forecasting with MSTL - calculating...

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- augurs contains several forecasting algorithms, including MSTL, which is a seasonal-trend decomposition - procedure - for modelling multiple seasonalities, based on LOESS. -

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- Forecasting with Prophet - calculating... -

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- augurs also contains an implementation of the Prophet - forecasting algorithm, which is a decomposable time series model with three main components: trend, - seasonality, - and holidays. -

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+ Outlier detection with DBSCAN - calculating... +

+

+ A similar but easier problem is to identify series that are + outliers: those which behave differently to the majority of + series. There are several algorithms; this demo shows the + DBSCAN algorithm. +

+
+
+
+ + + +
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+
+
+ + +
+

Forecasting with MSTL - calculating...

+

+ augurs contains several forecasting algorithms, including + MSTL, which is a seasonal-trend decomposition procedure for + modelling multiple seasonalities, based on LOESS. +

+
+
+
+ + + +
+
+
+
+ + +
+

+ Forecasting with Prophet - calculating... +

+

+ augurs also contains an implementation of the + Prophet + forecasting algorithm, which is a decomposable time series + model with three main components: trend, seasonality, and + holidays. +

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+ Changepoint detection with a Bayesian Normal Gamma - + calculating... +

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+ augurs also exposes functionality from the excellent + changepoint + crate, which provides a Bayesian approach to detecting + changepoints in time series data. Here, each changepoint is + highlighted in the plot. +

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- Changepoint detection with a Bayesian Normal Gamma - calculating... -

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- augurs also exposes functionality from the excellent changepoint crate, which provides a Bayesian - approach to detecting changepoints in time series data. - Here, each changepoint is highlighted in the plot. -

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