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Insights - Student version

Model. Predict. Simulate. Discover. Associate. Gain new Knowledge and Insights from Data.

In the Zotshop, Insights Advanced version.

The Research Tool for Predictive Modeling.


"Lovely maths and algorithms. Nice and simple product. Feel that it can significantly assist me." 

— Dr. Conrad Mackenzie, Australia


Insights is the next-generation, self-organizing, predictive modeling app for the Mac. Find unknown relevant relationships in your scientific, business or personal data stored in Excel, for example, by using outstanding, automated knowledge mining methods for prediction, classification, identification or simulation tasks in various fields.


Users in nearly any field without being an expert in modeling can analyze data sets and build powerful models, which help to gain new insights into complex phenomena, predict future behavior, simulate "what if" questions, and identify methods of controlling processes. Insights is used for predicting sales, production and demand, engineering problems, climate change, health or life sciences-related questions, or mining collections of data from government agencies.


Use the original and unique modeling and model evaluation features of Insights on your research data to extract new knowledge. Be more productive and get explicit model equations that describe your data. Run simulations on historical data stored in Excel for backtesting, for example, or use it on new data for forecasting processes several steps ahead. All results are reported and stored in Excel for further use.


Insights comes with documentation, extra literature, sample data and models such as:

  • Crude Oil price forecasting till 2025 and related questions 
  • Indoor temperature forecasting for energy consumption optimization
  • Monthly forecast of Global Mean Temperature and Ozone concentration till 2017
  • Fetal Monitoring and evaluation or prediction of ecotoxic effects of chemicals
  • Forecasting hourly bike sharing demand 48h ahead

Forecasting hourly bike sharing demand 48h ahead. Continuously. Every hour. Within seconds.



Feature Highlights:

  • High performance adaptive learning modeling and knowledge mining with ease
  • Hides the hard work, such as knowledge extraction, model development, validation, and variables selection, from user
  • Self-organizes regression models autonomously from up to 2000 inputs reliably and auto-generates the equation that describes the data
  • Similar Patterns sequential pattern recognition method for time process forecasting including autoupdating from Excel data sources
  • 64-bit parallel software out of the box that scales to the number of processor cores in your Mac

  • Live Prediction Validation technology
  • Implements models and model ensembles in Microsoft Excel or exports them to AppleScript or Text/MATLAB format

Models developed with Insights can be implemented in Excel, automatically, for further use.


Learn more about Insights to get all the details of the content above.


Why Do I Need a Model?
Humans have for centuries been seeking proxies for real processes. A substitute that can generate reliable information about a real system and its behaviour is called a model and they form the basis for any decision. It is worth building models to aid decision making, because models make it possible to:
  • Identify the relationships between cause and effect. This leads to a deeper understanding of the problem at hand by deriving an analytical relationship between them,
  • Predict the respective objects can expect over a finite future time span, but also to experiment with models. The ability to continuously make predictions from auto-associative past patterns is the core of human intelligence.
  • Simulate the objects' behaviour by experiment with models, and thus answer "what-if" questions essential to decision-making,
  • Control the objects by finding suitable means to effect the objects and enforce a specific behaviour.
There are many cases in practice where it is impossible to create analytical models using classical theoretical systems analysis or common statistical methods since there is incomplete knowledge of the processes involved. Environmental, medical and socio-economic systems are but three examples. In contrast, inductive models obtained by knowledge mining are derived from real physical data and represent the relationships implicit within the system without or with only little knowledge of the physical processes or mechanisms involved.
There are a lot of complex problems, which do need decision-making, but the means - the models - for understanding, predicting, simulating, and controlling such systems are missing increasingly, because we only have insufficient knowledge to follow theoretical modeling approaches. A powerful, proven in many applications, and easy-to-use tool that fills this knowledge gap is inductive, self-organizing modeling as implemented in Insights.
"I believe that tools like this are definitely the start of something very big in getting a handle on mountains of information." — Douglas, Dartmouth Medical School
"Insights is the most advanced implementation of the GMDH approach now. It uses the inductive method, which is different from deductive techniques used commonly for modeling on principle. Many important successful results were received using this tool recently. They show the advantage of it over analogous well-known software."
— Prof. Alexey G. Ivakhnenko, author of the GMDH approach
"I like Insights because its algorithm does not make any assumtions on the underlying data; well, at least not during the initial model-building phase. I also like the fact that it generates sets of equations that the user can review with detailed understanding of the interactions and dependencies of each variable. Also, the algorithm(s) behave surprising well under extreme conditions for certain complex dynamical systems. Congratulations for your excellent work."
— Alexis Pobedonostzeff, Pfizer Inc., Director, Health Care Issues Analysis & Management


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