Nicaise CHOUNGMO FOFACK
Ph.D, MSc, Ing
Time series forecasting under hard constraints
Data-driven business has gained an important attention in all-sized companies, since they can learn from their historical data what may happen in the near future. In this talk, we focus on Time Series Datasets which are very often used to describe a real-time activity. Precisely, we study the problem of “forecasting under hard constraints”, i.e. using minimal available information and part of data is missing. We derive an unsupervised machine learning framework and provide its implementation in a Maven project. Our solution requires no parameter tuning from the user.
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