This Modeling Trend, Cycles & Seasonality in Time Series Data using PROC UCM training course teaches how to model, interpret, and predict time series data using UCMs. The UCM procedure analyzes and forecasts equally spaced univariate time series data using the Unobserved Components Models (UCM).
By attending Modeling Trend, Cycles & Seasonality in Time Series Data using PROC UCM workshop, delegates will learn to:
- Analyze time series data using a novel class of models called the Unobserved Component Models (UCM). The UCMs decompose the response series into components such as trend, seasonals, cycles, and the regression effects due to predictor series.
- Use the UCM procedure to find a suitable model for the series of interest, to obtain extensive model diagnostics, and to generate series forecasts and the forecasts of the constituent components.
- Get detailed understanding of the series dynamics by analyzing the plots of the estimated components.
- Experience with regression modeling.
- Familiar with at least one time series modeling technique, such as Box and Jenkins or exponential smoothing.
- Some familiarity with SAS software.
This Modeling Trend, Cycles & Seasonality in Time Series Data using PROC UCM class is recommended for those who want to analyze time series data to uncover patterns such as trend, seasonal effects, and cycles using the latest techniques