Application of Time Series Forecasting and Machine Learning approaches for horticultural sales predictions

  • Abschlussarbeit
  • Straubing

Webseite TUM Campus Straubing, Professorship Bioinformatics

Predicting the future based on historical observations is a common problem in many areas. For this purpose, modern statistical and machine learning based methods for Time Series Forecasting are widely applied. In our research project, we focus on sales of small and medium-sized horticultural companies. The goal of this thesis is to apply already implemented classical Time Series Forecasting (e.g. Exponential Smoothing or ARIMA) and Machine Learning (e.g. XGBoost or LSTM) approaches to datasets provided by a partner company. Finally, you should draw a conclusion whether horticultural sales are predictable and methods generalize well across products and companies.

For more information, please see the linked document and feel free to contact us.

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