Sales prediction is an important part of modern business intelligence. First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality, many exogenous factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods.

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered. Time series analysis, especially forecasting, is an important problem of modern predictive analytics.

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered. Time series analysis, especially forecasting, is an important problem of modern predictive analytics.

It is very important to make sales forecasting in the supply chain management. In our previous post, we considered different approaches for time series forecasting. The most important thing is to make a decision how many products should be supplied into each store. If we can predict future sales precisely, the amount of products we need to supply is equal to our precise prediction. But in the real life we cannot make precise prediction, we rather can predict product consumption value with some confidential interval.

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered. Time series analysis, especially forecasting, is an important problem of modern predictive analytics.

In our previous post, we showed the examples of using linear models and machine learning approach for forecasting sales time series. Sometimes we need to forecast not only more probable values of sales but also their distribution. Especially we need it in the risk analysis for assessing different risks related to sales dynamics. In this case we need to take into account sales distributions and dependencies between sales time series features (e.g. One can consider sales as a stochastic variable with some marginal distributions.