Linear Models, Bayesian Inference, Machine Learning and Q-Learning In Time Series Forecasting
Tuesday, May 12, 2020
Time series describe many business, technical and industrial processes, they can be generated by different devices and can characterize consumers’ behaviour e.g. energy consumption. So time series-based predictive analytics is an important part in decision making processes in different industrial domain areas. Bohdan is going to consider different use cases, including energy consumption, which depend on many exogenous factors like weather, building option, etc. and we are going to consider different approaches: linear models, bayesian inference and machine learning. They can be propagated on a wide range of business and technical problems. Probabilistic models based on the bayesian inference can take into account expert opinion via prior distributions for parameters and can be used for different kinds of risk assessments. Multilevel predictive ensembles of models based on the bagging and stacking approaches will be regarded. Bohdan is also going to consider a Q-learning approach which can be used in many industrial problems where it is necessary to obtain the sequences of optimal decisions.