  Distributed Stream Processing (DSP) systems enable processing large streams
of continuous data to produce results in near to real time. They are an
essential part of many data-intensive applications and analytics platforms. The
rate at which events arrive at DSP systems can vary considerably over time,
which may be due to trends, cyclic, and seasonal patterns within the data
streams. A priori knowledge of incoming workloads enables proactive approaches
to resource management and optimization tasks such as dynamic scaling, live
migration of resources, and the tuning of configuration parameters during
run-times, thus leading to a potentially better Quality of Service.
  In this paper we conduct a comprehensive evaluation of different load
prediction techniques for DSP jobs. We identify three use-cases and formulate
requirements for making load predictions specific to DSP jobs. Automatically
optimized classical and Deep Learning methods are being evaluated on nine
different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster
monitoring. We compare model performance with respect to overall accuracy and
training duration. Our results show that the Deep Learning methods provide the
most accurate load predictions for the majority of the evaluated datasets.
