Discover correlations, compute correlation measures, and rank variables by their correlations with a target.
Spot any pattern from slow trends to sudden jumps for hundreds of time series, and overlay moving averages in your graphs.
Compare anomalous time periods to normal operation, and identify process parameters that have changed – to explain anomalies, or increased scrap production.
Compare time periods in detail by their graphs, statistics, distributions, statistical tests, and much more.
Assess the completeness of your data at a glance and analyze the distribution of gaps in detail.
Discover anomalous data for further analysis or cleanse your data by removing outliers and spikes with a single click.
Summarize your data by products, days, or any other data categories and visualize your summaries using various chart types.
Characterize your data by numerous statistical measures and compare the statistics for any parts of the data.
Express relations between variables by regression functions which update instantly upon interactive data selection.
Compute new data with maximal flexibility by using powerful functions and embedded Python code.
Derive new time series via smoothing, time-shifting, and many more functions and tune their parameters at any time.
Search recurring patterns in time series automatically and compare up to thousands of them for trends and anomalies.
Build multivariate regression models automatically or step-by-step and study dependencies in your data in detail.
Specify setpoints and tolerance limits for visualization and break down process KPIs along time, products, grades, and much more.
Analyze thousands of curves from machine operations or measured spectra and correlate them with contextual information such as process and quality parameters.
Extract meaningful curve features by intuitive specification in the graphics and include these KPIs in your analysis.
Monitor the performance of many predictive models, compare them in detail, and discover new ways for optimization.
Employ advanced analytics to detect anomalies in time series and see their distribution at a glance.