Time Series Labeling
with Visplore

Data labeling is a critical step in building high-quality AI models.

Visplore is a graphical tool for interactive labeling and exploring massive multi-variate time series data.

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See 3 Minutes Of Time Series Labeling In Action

Interactive data labeling
Boost your AI models by better data labels

All Visplore versions feature powerful interactive tools for data labeling. These tools will help you to select and label time series data without worrying about complex parameters.

Select and label large amounts of multivariate data
Define labels based on integrated formulas or scripts
Mark up parts of the data directly in the visualization
Import categorical data attributes for auditing or fine-tuning labels

Compare multiple labeling results in depth
Share label definitions easily with colleagues
Start labeling directly from Python, Matlab or R
Use pattern search to label all occurrences of selected patterns efficiently

Read in our blog how AI projects benefit from high-quality labels for time series data.

Different existing data labels can easily be audited and cleared at any time
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Labeling rules can be applied to new data and patterns can easily be searched within seconds.
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Want to learn more and see concrete use cases? Watch our webinar on data labeling (no registration required):

You still have questions or want to try it without limits in a free trial of Visplore Professional? We are happy to get in touch with you!

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Data Labeling with Visplore

The quality of input data is one of the most important factors in AI, machine learning, and data mining.

Building high-quality predictive models requires data which truly and comprehensively reflects the phenomenon that should be modelled or mined. AI algorithms typically do not detect implausible data on their own, but rely on the quality of data preselection and the quality of their labels. For predictive maintenance, for example, it may be necessary to label data from industrial processes as “good”, “critical”, “anomalous”, and so on, before building classification models. In practice, however, selection and labeling of data can be very time-consuming, difficult to address by scripted logic, and may require many iterations with domain experts.

Visplore reduces those challenges to a minimum – and saves you valuable time while improving the labeling results.

To get started with data labeling read our step-by-step guide!

Working with data labels

Data labeling is a critical step in building high-quality AI models. The purpose of data labeling is to make information about distinguished classes (such as machine states) explicit. This information is important to train an AI model for the automatic distinction of states. AI experts emphasize that optimizing the input data is at least as powerful for optimizing the prediction quality as fine-tuning model parameters. A special case of labeling is the selection of appropriate input data. For example, unsuitable input data such as interruptions of the machine operation should be excluded before training any model on the data.

Why our customers
prefer Visplore

Perfect for dirty sensor data. With Visplore, we give customers more insights for the same money.

Research Engineer, R&D Consultancy Firm

When we need it fast, we use Visplore.

Data Scientist, Hydropower Company

Visplore enabled us to gain significant new insights from massive data about the impact of production process parameters on product quality.

Process Expert, Refractories Enterprise

Thanks to its ease of use and high performance even with millions of measurements, Visplore has established itself as our standard tool for quality management in plants from China to South America.

Head of R&D, Refractories Enterprise

Checking and cleaning data from energy systems used to be very time-consuming and kept us from our core tasks. With Visplore, data preparation has become much faster, and the quality of the resulting data has improved.

Leader of Energy Innovation Lab

The good usability and the comprehensive visualizations enabled us to gain a deeper understanding of routinely collected data and their relationships. For example, this increased the quality of forecast models.

Expert Renewable Energy, Transmission System Operator

Visplore is recognised and in use as a central strategic software for data analytics throughout the company.

Coordinator of Renewable Energy Data, Transmission System Operator

By analyzing process data from a large foundry with Visplore, complex dependencies were detected that we didn’t find with other software. As a result, our customer’s heat energy was reduced by around 5% and mold wear by around 4%.

Process Expert, Foundry Research Institute

For our thermodynamical simulations, Visplore helps us to identify suitable data, eliminate outliers, and analyze physical processes very effectively.

Manager Process Engineering, Thermal Energy Company

The visualization is fast and simple enough for live collaboration with the customers, on site. By this, Visplore supports a thorough understanding of correlations, outliers, and anomalies.

Head of Research in Industrial Data Science, R&D Research Institute

Visplore makes our analysis of sustainable energy systems much more efficient.
In addition to the performance for large data, its integration with scripting environments is a big plus.

Research Engineer, Energy Research Center

Visplore allows for a much easier investigation of relevant patterns and structures. Our downstream analysis becomes more efficient and we gain more confidence in the results.

Main Data Scientist, Hydropower Provider