Nonparametric time series forecasting with dynamic updating
SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science.
This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code.
Historical transactional data from the Finance and Operations transactional database is gathered and populates a staging table.
This staging table is later fed to a Machine Learning service.
Therefore, you can generate demand forecasts that consider historical data that is spread among multiple systems.
However, the master data, such as item names and units of measure, must be the same across the various data sources.
Demand forecast generation starts in Finance and Operations.Advances in data collection and storage have tremendously increased the presence of functional data, whose graphical representations are curves, images or shapes.As a new area of statistics, functional data analysis extends existing methodologies and theories from the realms of functional analysis, generalized linear model, multivariate data analysis, nonparametric statistics, regression models and many others.The forecasts, historical data, and any changes that were made to the demand forecasts in previous iterations are then available in Finance and Operations.You can use Finance and Operations to visualize and modify the baseline forecasts.