Entwicklung einer kontextsensitiven Prozessvorhersage unter Berücksichtigung bestehender Deep Learning Ansätze
Are you interested in a modern, practical, yet theoretically based thesis? Then read on. Customer movements in ski resorts represent logistical processes, which describe the flow of objects (e.g., persons or resources) within networks through time and location. To date there exist no good prediction systems for customer movements in large tourism industries, such as ski resorts, amusement parks or public transport. One promising approach to predict these processes on a large scale are Recurrent Neural Networks. One major advantage of these networks is, that they can easily incorporate contextual information through additional inputs.
The goal of this thesis is to implement a general process prediction system based on Recurrent Neural Networks and test it on an existing data set of a ski resort. For the implementation, it is helpful if the student has already some proficiency in or the willingness to learn Python/Java and Apache Spark or MPI/Open MPI.
If you are interested in the topic, please do not hesitate to contact me.