We identify and prepare relevant data sets needed for analytics applications. Then we build analytical models grounded in statistical theory using analytical software and programming languages such as R, SAS, and MATLAB. Training and testing data sets are developed to assess their findings. Other examples include:
- Data mining
- Predictive modeling
- Machine learning
- Time series regression and forecasting
- Cross-sectional and spatial time series
Below is an example of Yellow Grease (YG) and Choice White Grease (CWG) monthly price forecast.
We first identify an appropriate historical time series price data on yellow grease and choice white grease to train Generalized Linear Mixed Models. We then use the parameters of the training dataset for the prediction and forecast using R as shown below.