Analytics of Business Time Series Using Machine Learning and Bayesian Inference
This is an incremental survey for business analytics practitioners, summarizing existing applications without new findings.
The paper surveys multiple case studies applying machine learning and Bayesian inference to business time series, including sales forecasting, dynamic optimization, and modeling of Bitcoin prices and COVID-19 impacts, but does not report specific results or numbers.
In the survey we consider the case studies on sales time series forecasting, the deep learning approach for forecasting non-stationary time series using time trend correction, dynamic price and supply optimization using Q-learning, Bitcoin price modeling, COVID-19 spread impact on stock market, using social networks signals in analytics. The use of machine learning and Bayesian inference in predictive analytics has been analyzed.