Data Analysis

Analysis of data is the process of studying, examining, comparing, interpreting and looking at the data in order to get great and deep insights about the real-world problems and phenomena. Data analysis extracts and uncovers helpful and valuable information which facilitates decision-making processes.

At Eris Innovation we apply the modern data science to everyday problems and challenges, using the ultimate data analysis tools and techniques which has made us one of the main solution providers in the current private sector.

Predictive Modeling

The high proficiency in Predictive Modeling has helped us to develop some accurate and novel Forecasting methods which delivered quite acceptable and encouraging results in the field of Energy Price Forecasting. These ingenious techniques enjoy the benefits of the human being's neural system as well as the human reasoning capacities that increase our enhanced capabilities for making accurate and reliable predictions of economic indicators, product's sales, good's prices and customer demands.


Optimization is the process of maximizing the performance and efficiency of a system, while minimizing the costs without violating the system's constraints. Optimization techniques provide a mathematical model of a business which quantifies the tradeoffs between numerous objectives and bring valuable help to decision makers. At Eris Innovation we are highly experienced in solving several complex and multi-objective optimization problems applying different optimization algorithms ranging from statistical approaches like Non-Linear Mixed-Integer Programming (NLMIP) to metaheuristics such as Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO).

Anomaly Detection

Anomaly Detection is the identification of unexpected or abnormal behavior in the data, also referred to as deviations or outliers. Anomalous data points could be potential faults or failures in the system indicating the possible financial/banking fraud, health problems in a society, cyber-security attacks and etc. In data mining, anomalies do not necessarily represent failures and sometimes they are only indicative of some new trends or behaviors for example in the consumer buying habits.