I'm a Sr. Data Scientist at Daimler AG's Finance department and an external researcher at the Data and Web Science Group of the University of Mannheim.
My topics of interest lie in the fields of artificial intelligence and data science. My research deals with time series forecasting, ensemble learning, and the combination of the two.
Working towards the AI-powered transformation of controlling and reporting in the Daimler Trucks unit. Besides heavy process automation, we focus on estimating the future development of relevant KPIs, collaborating closely with the respective business departments in an international environment. Tasks and tools include:
Tasks and projects (excerpt):
Title: "Dealing with Missing Values in Time Series"
Writing my master thesis about dealing with missing values in time series data with a focus on multivariate sensor data that was collected at irregularly spaced time intervals. Besides the massive amount of data, a major challenge is finding appropriate ways of imputing missing data gaps in the series.
Specialization track: Data & Web Science with a focus on Data Mining, Machine Learning, Information Retrieval/Extraction and Business Intelligence.
Master Thesis: "Dealing with Missing Values in Time Series" in cooperation with the Robert Bosch GmbH.
Specialization track: Economics (focus on statistics, econometrics, macroeconomics).
The bachelor thesis reviewed methods and applications of data compression techniques, such as used in the ZIP file format.
Currently investigating state of the art forecasting models like recurrent neural networks (more precisely, LSTMs). Figuring out how to design ensembles of such models in order to harness the "wisdom of the crowds". The objective is to design an ensemble framework that is robust to the underlying data generation process. Major challenges are:
Please drop a mail to mail*at*saschakrstanovic.com for any inquiries.