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.
翻译:在调查中,我们考虑了关于销售时间序列预测的案例研究,即利用时间趋势校正、动态价格和供应优化,利用Q-学习、比特币价格模型、COVID-19对股票市场的影响,利用社交网络分析信号预测非静止时间序列的深层学习方法,分析了在预测分析中使用机器学习和巴耶斯语推论。