Automated No-Code Predictive Model-Builder in a Jupyter Notebook

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Ai-Rover For Time Series Data

Businesses have moved well beyond wondering what happened or why something happened. To be successful in today’s hypercompetitive global market, they need to understand what will happen and what specific actions will drive the business value of predictive (and prescriptive) analytics.

Data — specifically, time-series data — is everywhere. It’s collected by websites, security and traffic cameras, machines, and Internet of Things (IoT) sensors, among other sources. The challenge today lies not in collecting data, but in gaining actionable insights from data with predictive analytics. 

Predictive analytics can be applied to core business processes in any industry, such as sales forecasting and demand planning in retail, electricity production and consumption in the energy sector, fraud detection and credit risk in finance, and asset health monitoring as predictive and preventive maintenance in manufacturing. 



Many organizations have limited skills and experience to effectively implement machine learning (ML) and predictive analytics. As result, ML and predictive analytics are often seen as an experimental playground. Implementations don’t make it to production; instead, they get labeled as a “nice try.” Organizations often view ML as an innovative and complex domain. Although many people realize that it has great potential, this potential is often deemed to be a long way off. All this contributes to ML projects getting stuck in the experimental stage.


Ai-Rover™ For Time Series Data


The Ai-Rover™ For Time Series Data is an automatic model-building solution designed specifically for time-series data. Based on this data, Ai-Rover™ extracts relevant features and builds explainable forecasting and anomaly-detection models.

The Ai-Rover™ For Time Series Data Engine is built on a field of mathematics called information geometry, an interdisciplinary field that uses differential geometry techniques to study probability theory and statistics. 

The Ai-Rover™ For Time Series Data Engine creates models in a single step — from feature engineering to model building and deployment. This highly automated approach to time-series modeling is called InstantML (OEM from Tangent Works). The high level of automation reduces the time needed for model building, as well as the engineering effort and mathematical expertise required.

Getting Results Fast

Delivers results within Seconds or at most, a few minutes with one step model creation

Automating the Model Building Process

Automates the feature engineering process, analyzing the historical input data and determining which features are relevant given the use case, without the need for dedicated expertise

Generating Accurate Models

Automation allows the engine to create models with high accuracy in a matter of minutes

Explaining Model Insights

Generates transparent, human-readable models that provide users with comprehensive insights into the models created and helps them measure the impact of predictors and features on target values — automatically and instantly

Use Cases

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Supply Chain Optimization Notebook card.
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Marketing & Advertising Campaign Effecti