Widgets for time-series analytics
We introduce Push, an innovative framework designed specifically for interactive time series analysis. This framework empowers regular business users and part-time analysts to conduct sophisticated data analyses effortlessly.
In data science, analysis of time series data plays an important role in understanding temporal patterns, finding correlations and making informed predictions. Interactive widgets and notebooks offer new possibilities to quickly refine model parameters, improve the visualization of data and to iterate on existing analysis.
This capability not only enhances the flexibility and efficiency of time series analysis but also enables more effective exploration of data nuances and hypotheses. By leveraging widgets, analysts can expedite their workflows, gain deeper insights, and ultimately make more accurate predictions.
We introduce Push, an innovative framework designed specifically for interactive time series analysis. This framework empowers regular business users and part-time analysts to conduct sophisticated data analyses effortlessly. Push leverages modern standards and can be used standalone, but also provides seamless integration with popular data science environments like RStudio and Notebooks. All visualizations can also be easily embedded in commercial data science environments like Tableau.
Technically, it is based on the popular frameworks Glimmer and Ember, provides fast data loading via Arquero and enables visualization with the exceptional Vega library.
This sample summarizes data based on the latest date and also provides benchmarking across different groups.
You can also have a look at the storybook featuring all components. The complete Github Repository is available at https://github.com/matt-do-it/push