Webseite TUM Campus Straubing, Professorship Bioinformatics
Predicting the future based on historical observations is a common problem in many areas. A potential way to implement this are Time Series Forecasting methods. Some of these combine global and local patterns present in the data. Global effects might be identified in related time series, e.g. from the same domain. These can be enriched with local patterns, e.g. of a specific company, to provide final predictions. There are several approaches in literature which make use of this idea. The goal of this thesis is to evaluate their applicability to sales predictions of small and medium-sized horticultural companies.
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