PROQNOSTIX is a project of ask-a-woman.com, an expert network.

Welcome to Proqnostix: Your Compass to the Future of Data Analytics and AI-Driven Market Predictions

In a world where data is the new gold, Proqnostix opens the gates to an immense treasure trove of knowledge and foresight. We are more than just a company; we are your trusted partner in the journey towards precise, data-driven decisions. With a profound understanding of the power of Data Analytics and Artificial Intelligence (AI), we stand at the forefront of a revolution in market prediction.

Our Mission: Your Success

Proqnostix is dedicated to empowering businesses of all sizes and sectors not just to comprehend today’s market, but also to anticipate tomorrow’s trends. Our services and products are designed to provide you not just with data, but with genuine insights.

What We Offer

  • Tailored Market Predictions: We gather and analyze market information to create customized predictions that give your business a critical edge.
  • Empowerment Through Technology: Our advanced tools and platforms enable your team to generate and refine their own forecasts, keeping you independent and informed.
  • Science Meets Application: Our approach marries scientific accuracy with practical applicability, ensuring that every prediction is not just precise, but also actionable.
  • A User Interface You’ll Love

We believe that powerful analysis should go hand in hand with an intuitive, user-friendly interface. That’s why we’ve designed our platform with a focus on User Experience (UX), to provide you with a seamless, efficient, and enjoyable usage experience.

Join the Proqnostix Community

Be part of a community at the cutting edge of technological innovation. Our regular articles, case studies, and insights offer you a continuous flow of knowledge, keeping you abreast of the latest trends and techniques in the world of Data Analytics and AI.

Ready to explore the market of tomorrow? Dive into the world of Proqnostix – where your data shapes the future.

Welcome to the Dynamic World of PROQNOSTIX: Your Gateway to Precision in Prognostics and Predictive Analytics

In the realm of PROQNOSTIX, we navigate through a spectrum of pivotal terms and concepts, each a cornerstone in our journey of data-driven foresight:

  • Prognoses
  • Predictions
  • Forecasts
  • Estimations
  • Simulations

Prognosis is defined as “a forecast of the future course, or outcome, of a situation; a prediction” (Daigle 2014, p. 7). It’s a term often resonating in the medical field, forecasting the likely progression and result of a condition, backed by current and historical data.

Prognostics, on the other hand, is the art and science of predicting future events, hinging on anticipated usage and environmental conditions, as a cornerstone for a system’s resilience and efficient operation (Sankararaman/Abhinav/Goebel 2014, p. 533).

To Prognosticate means “to foretell from signs or symptoms: predict” (Merriam Webster Dictionary 2019).

Predictions and Forecasting are intertwined yet distinct. Prediction is about estimating outcomes for unseen data, fitting models to training data sets for future projections (Döring 2018). Forecasting, increasingly a buzzword in the German-speaking world, focuses on future predictions based on time-series data (Döring 2018).

While there are no rigid boundaries defining these terms, they each hold a unique place in the world of mathematical modeling and statistics:

  1. Prognosis: Specific to predicting the course and outcome of a particular state, often in medical contexts.
  2. Forecasting: Utilizes historical data and statistical models to predict future events, like weather forecasting.
  3. Prediction: A broad term involving the use of models to predict unknown or future data.
  4. Estimation: Determining values for unknown quantities based on known data.
  5. Simulation: Replicating real processes or systems over time.

In summary, at PROQNOSTIX, we harness these concepts to deliver precise, reliable, and actionable insights. Whether it’s forecasting market trends, predicting customer behavior, estimating key business metrics, or simulating market scenarios, we are your trusted partner in navigating the complexities of today’s data-driven world. Join us as we reshape the future with data.

The tree view presented in Figure 1 offers a comprehensive visualization of forecasting, showcasing its various branches and sub-branches. This includes key components such as definitions, the six principal elements, requirements, and both quantitative and qualitative methods. This layout effectively illustrates the multifaceted nature of forecasting, providing a clear and structured overview of its many aspects. (see Figure 1),

Fig. 1: Core Aspects in Forecasting

6 key principals of forecasting

Forecasting, an essential discipline in various fields like economics, meteorology, and business planning, relies on several key principles to enhance its accuracy and relevance. Let’s delve into the six key principles you’ve mentioned: judgment, tangibilization, prudence, disaggregation, iteration, and triangulation.

1. Judgment

  • Definition: Judgment in forecasting refers to the use of expert knowledge and intuition in making predictions.
  • Application: It involves leveraging the experience and expertise of individuals to interpret data, understand trends, and make informed predictions. Judgment is particularly crucial in situations where historical data may not fully capture future conditions or when dealing with unprecedented scenarios.
  • Benefits: It adds a human element to the forecasting process, allowing for the incorporation of non-quantifiable factors like market dynamics, consumer behavior, or political changes.

2. Tangibilization

  • Definition: This is the process of making abstract forecasts more concrete and understandable.
  • Application: Tangibilization can be achieved through visualization techniques, such as graphs and charts, or by translating forecasts into physical models or prototypes. This principle is especially useful in fields like product development or urban planning.
  • Benefits: It helps stakeholders better understand and relate to the forecasts, thereby facilitating more informed decision-making.

3. Prudence

  • Definition: Prudence in forecasting involves a cautious and conservative approach, especially in the face of uncertainty.
  • Application: This means acknowledging the limits of predictive models and avoiding overconfidence in their outputs. It often involves preparing for a range of possible outcomes and considering worst-case scenarios.
  • Benefits: Prudent forecasting helps in risk management and prevents overreliance on overly optimistic scenarios.

4. Disaggregation

  • Definition: Disaggregation is breaking down complex forecasts into simpler, more manageable components.
  • Application: Instead of making a single, broad forecast, disaggregation involves making separate predictions for various elements or time periods. For example, instead of forecasting annual sales, a company might forecast monthly sales for different product categories.
  • Benefits: This approach can improve accuracy as it allows for a more detailed analysis of each component. It also makes it easier to identify the specific drivers of change.

5. Iteration

  • Definition: Iteration refers to the process of repeatedly revising forecasts based on new data and insights.
  • Application: As new information becomes available, forecasts are updated to reflect these changes. This is common in long-term projects where conditions can change significantly over time.
  • Benefits: Iterative forecasting ensures that predictions remain relevant and accurate, adapting to new trends and data.

6. Triangulation

  • Definition: Triangulation in forecasting means using multiple methods or data sources to validate a forecast.
  • Application: This might involve combining quantitative models with qualitative insights, or using different statistical methods to arrive at a forecast. For instance, a market forecast might be based on both econometric modeling and consumer surveys.
  • Benefits: Triangulation increases the reliability of forecasts by cross-verifying them through different lenses. This approach helps in mitigating biases and errors that might arise from relying on a single method or source.

Each of these principles plays a critical role in enhancing the reliability and effectiveness of forecasting. By integrating judgment with data, making abstract predictions tangible, adopting a prudent approach, breaking down complex forecasts, revisiting predictions iteratively, and validating forecasts through multiple methods, forecasters can significantly improve the accuracy and utility of their predictions.

Quantitative and qualitative methods in forecasting

The role of quantitative and qualitative methods in forecasting is crucial, as they offer different perspectives and approaches to predicting future events or states. Each method has its strengths and is often used complementarily to enhance the accuracy and reliability of forecasts.

Quantitative Methods

Quantitative methods rely on numerical data and statistical models. They are particularly useful when large amounts of historical data are available.

  • Time Series Models:
    • Role: These models analyze time-ordered data sets to identify patterns such as trends, seasonal variations, or cycles. They are fundamental in predictions in areas like financial market analysis, weather forecasting, and sales predictions.
    • Examples: Autoregressive models (AR), Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA).
  • Causal Models:
    • Role: They attempt to identify and quantify cause-and-effect relationships between variables. Useful for understanding how changes in one variable (e.g., price, advertising expenditure) impact another (e.g., sales figures).
    • Examples: Regression analysis, econometric models.
  • Experimental Models:
    • Role: Based on controlled experiments to test the effects of specific variable changes. Particularly useful for examining causal relationships in a controlled environment.
    • Examples: A/B testing, randomized controlled trials.

Qualitative Methods

Qualitative methods rely on non-numerical data such as expert opinions, case studies, or surveys. They are especially valuable in new, unexplored areas or complex scenarios that are not easily quantifiable.

  • Surveys:
    • Role: Collecting information directly from individuals, typically through questionnaires or interviews. Surveys are useful for gaining insights into consumer preferences, opinions, and behaviors.
    • Examples: Market surveys, customer satisfaction studies.
  • Delphi Method:
    • Role: A structured communication technique often used in forecasting future events by iteratively querying and consolidating expert opinions over several rounds.
    • Examples: Technology trend analysis, long-term strategy development.
  • Expert Opinions:
    • Role: Gathering assessments from professionals in a particular field. Useful when historical data are limited or non-existent, or when investigating a new phenomenon.
    • Examples: Market analysis, political risk assessment.
  • Market Experiments:
    • Role: Real-world tests in a market environment to study reactions to product changes, new advertising campaigns, or other marketing initiatives.
    • Examples: Pilot projects, market launch tests of new products.

Integration of Quantitative and Qualitative Methods

  • Complementary Strengths: Quantitative methods offer precise, data-based predictions, while qualitative methods provide deeper insights and contextual information.
  • Holistic Approach: Combining both approaches leads to a more comprehensive understanding and can improve the accuracy of predictions.
  • Adaptation to Specific Needs: Depending on the situation and data availability, forecasting models can be tailored to meet relevant needs.

Requirements Management for Forecasting and Prognoses

In the realm of forecasting and prediction within a business context, various requirements must be considered to ensure effective and reliable projections. These requirements include business objectives, method descriptions, typical approaches, costs, accuracy, identification of turning points, necessary data, development time for a suitable application, and references. Here is a more detailed examination:

Business Objectives:

  • Understanding the purpose of the forecast is crucial. This could involve optimizing inventory levels, pricing strategies, market entry strategies, or other business decisions.

Method Description:

  • A clear definition of the quantitative and qualitative methods used is essential. This includes time series analysis, causal models, Delphi methods, and other relevant approaches.

Typical Approaches:

  • Depending on the industry and specific requirements, various approaches like trend analyses, econometric models, or machine learning can be applied.

Costs of Forecast Development:

  • Developing and maintaining forecast models incurs costs, encompassing both direct expenditures for tools and personnel, and indirect costs such as training.


  • The expected accuracy of the forecast must be defined, often depending on the type of prediction and the availability of data.

Identification of Turning Points:

  • The ability to recognize significant changes in the market or data trend line is crucial for the adaptability and relevance of forecasts.

Required Data:

  • It must be clear what kind of data is needed (historical data, real-time data), as well as their sources and quality.

Time Required to Develop a Forecasting Application:

  • The development duration of a forecasting tool can vary, depending on the complexity of the model and its integration into existing systems.

References and Case Studies:

  • Previous successful implementations or case studies can serve as a reference and help build confidence in the applied methods and approaches.

These requirements must be carefully weighed and tailored to the specific needs of the business. Effective forecasting requires not only appropriate methods and data but also a clear understanding of business objectives and market conditions.

DevOps and Big Data

In the era of big data and big code numerous open source tools, libraries, frameworks and code repositories allow IT experts to follow the DevOps approach, where time consuming developing tasks such as deployment, installation, configuration and the set up are automatized.

“Big Code” and open-source Tools

Code repositories offer pre-built, refactorable and reusable working examples for the easy usage within the enterprise architecture. A large set of standardized models, methods, techniques and practices allow developers and testers to perform mathematical operations and analysis such as forecasting and AI with an ease on large amounts of data. The magic lies in the huge number of free usable open source tools and libraries with to access to the APIs of numerous providers of data and code.

Predictive Analytics

PROQNOSTIX enables companies to perform analysis on data, driven by a business goal to predict future developments based on the data, most appropriate regarding the business goals from the perspective of predictive analytics.

Discipline of Econometrics

Thus PROQNOSTIX offers products and services in the discipline of econometrics, defined as “the forecasting of macroeconomic variables, such as interest rates, inflation rates, gross domestic product (GDP) and a collection of methods for the forecasting of economic time series and the prediction of economic theories.” (cf. Wooldridge 2009, p. 1f)

Data can be structured as time series or as cross-sectionnal data.

Time Series Data

Time series data consist of observations on stock prices, money supply, consumer price index, annual homecide rates, automobile sales etc. (cf. Wooldridge 2009, p. 8)

Cross-sectional Data

“Cross-sectional data consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. Sometimes, the data on all units do not correspond to precisely the same time period. For example, several families may be surveyed during different weeks within a year, In a pure cross-sectional analysis, we would ignore any minor timing differences in collecting the data. If a set of families was surveyed during different weeks of the same year, we would still view this as a cross-sectional data set.” (cf. Wooldridge 2009, p. 5)

Here you can book our workshop:




Wooldridge, Jeffrey, M. (2009): Introductory Econometrics. A modern approach. South-Western. Cengage Learning. Fourth Edition.

The Art of Forecasts and Predictions

The arts of forecasting is the determination of the best fitting model with the most adequate parameters for a given business goal. There is a large set of models in AI and further emerging technologies, such as blockchain, IoT, serverless computing etc. to be discovered.

Modeling Forecasts

All models will perform differently depending on which application domain they are applied to. The job of the forecaster is to compare those models most suitable for a given issue and to optimize the parameters of the model. The next figure shows an hierarchical overview of widely used forecasting methods and the underlying taxonomy (see Fig. 1):

Industrial Branches

Potentially each industrial branch is affected by the potential for competitiveness. We now focus on three branches in order to demonstrate the potential of the application of products and services of PROQNOSTIX:

  • Banking Industry:
    • Forecastings for investments in the stock markets
    • Forecastings of the creditworthiness
  • Geodata
    • Real estate prices
    • Demographics
    • etc.
  • Marketing
    • Search Engine Optimization
    • Social Media Analysis
    • etc.

Here you can book our workshop:

How PROQNOSTIX proceeds with nubank.de

Nubank.de is a side project of ask-a-woman.com (AAW). Nubank.de typifies a model bank on the basis of a scientific research conducted by experts of AAW. In this research project

  1. PROQNOSTIX supports the definition of the companies business goals cooperating with the domain experts of nubank.de. The evaluation of the potential of big data repositories for the application of mathematical and AI-based methods and practices e. g. for forecasts performed in an software engineering environment provided by PROQNOSTIX is part of the analysis stage.
  2. Definition of technology stack, methods, libraries, tools, resources, tasks, roles etc.
  3. Agile Data Science (ADS): Project management and software engineering process is based on the ADS approach.
  4. Hybrid design of products and services:
    1. Delivery of a running software environment with a software lifecycle.
    2. Inhouse workshops for the usage of big data, open source software and methods of software engineering and mathematics in companies.

Case Study: nubank.de gives an order

The following procedure model is applied:

I Preanalysis

  • First survey and interviews
  • First suggestions and recommendations:
    • A) Investment strategies on the stock markets based on numerous key performance indicators of companies and feasibility analysis for mathematical methods.
    • B) Calculation of the creditworthiness of private persons and companies based on data (Schufa, Social Media, self-declerations)
    • C) Potential analysis on emerging technologies, such as Blockchain, Serverless Computing, IoT, VR/AR/MR etc.
    • D) Designation of workshop content
  • Milestone: Go?

II Requirement Engineering

III Design

IV Model

V Implementation

VI Test

VII Evolution


The course content is divided into three parts:

  • I) Application Areas
  • II) Methods, Techniques and Best Practices
  • III) Future Potential of Emerging Technologies

The workshops are bookable in packages of

  • 1 day á 6 hours (Light Package) or
  • 3 days á 6 hours (Full Package)
  • Other offers are available

The price for a workshop is 150€/h plus taxes:

  • Light Package (900€ plus taxes)
  • Full Package (2700€ plus taxes).


We first give an overview on potential application areas and then focus on the requesting companies branch.

As an example the banking industry is likely to be interested in

  • forecasts for investments in the stock markets and therefore the companies key performance indicators,
  • forecasts of the creditworthiness of a private person or a company,
  • the application of AI methods etc.

The Geo Information Systems (GIS) sector is applying forecasts and other mathematical methods on the development of

  • Real estate prices
  • Demographics
  • etc.

And marketing departments and agencies are interested in the success of their campaigns in the fields of

  • Search Engine Optimization
  • Social Media Analytics
  • etc.


In the second part of the workshop we address primarily methods, techniques and best practices of Mathematics, Informatics and Business administration, but we´ll also take into account methods from other branches.

Methods, techniques and practices of mathematics are as follows:

  • Statistics
  • Artificial Intelligence, Machine Learning, Deep Learning
  • Libraries & Tools


  • Tool Portfolio
  • Software Engineering
  • Modeling
  • Programming Languages

The discipline of Business Administration serves companies with methods such as

  • Project Management,
  • Controlling,
  • Logistics,
  • Sales
  • etc.

Potentially any methods, techniques and best practices of any other discipline or branch, such as law, health sector, entertainment sector, car industry etc. can be listed here.


The third part of the workshop enlightens the potential of emerging technologies, elaborates and prognosticates opportunities out of the following emerging technologies:

  • The blockchain is obvious to be the technology with the most potential impact on transaction processes of the future. Since transactions are the core of entrepreneurial interactions with the customers and employees, this subject will also be evaluated for the attainment of defined business goals.
  • The Internet of Things (IoT) has growingly significance for the future markets. A prognosis on the potential for the given company will be conducted during the workshop.
  • Bots undertake more and more tasks in the interaction between companies and customers, reducing increasingly costs e. g. in the support departments.
  • Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR) offer new opportunities for the creation of raised user experiences with products and services of companies for private households and industries.
  • Further Emerging Technologies such as 3D- and 4D-printing, Autonomous Vehicles (AV), Drones, Transhumanism etc. will be discussed, too.