A Framework for Time Series Preprocessing and History-based Forecasting Method Recommendation

The complexity of managing the capacities of large IT infrastructures is constantly increasing as more network devices are connected. This task can no longer be performed manually, so the system must be monitored at runtime and estimations of future conditions must be made automatically. However, since using a single forecasting method typically performs poorly, this paper presents a framework for forecasting univariate network device workload traces using multiple forecasting methods. First, the time series are preprocessed by imputing missing data and removing anomalies. Then, different features are derived from the univariate time series, depending on the type of forecasting method. In addition, a recommendation approach for selecting the most suitable forecasting method from this set of algorithms for each time series based only on its historical values is proposed. For this purpose, the performance of the forecasting methods is approximated using the historical data of the respective time series under consideration. The framework is used in the FedCSIS 2020 Challenge and shows good forecasting quality with an average R2 score of 0.2575 on the small test data set.