SAS Forecast Server Procedures

1. Time Series Forecasting: Utilizes various techniques for accurate time series forecasting.
2. Automatic Model Selection: Automatically selects the best forecasting model based on data characteristics.
3. Seasonality and Trend Detection: Identifies and incorporates seasonality and trend patterns.
4. Outlier Detection: Detects and handles outliers to ensure robust forecasts.

Before learning SAS Forecast Server Procedures, it's beneficial to have the following skills:

1. Data Analysis: Understanding of basic data analysis concepts and techniques.
2. Statistical Modeling: Familiarity with statistical modeling techniques such as regression analysis and time series analysis.
3. SAS Programming: Proficiency in SAS programming language, including data manipulation and statistical procedures.
4. Forecasting Concepts: Knowledge of forecasting concepts and methodologies.

By learning SAS Forecast Server Procedures, you gain the following skills:

1. Advanced Forecasting Techniques: Mastery of advanced forecasting techniques such as time series analysis, exponential smoothing, and regression modeling.
2. Model Selection: Ability to select the most appropriate forecasting model based on data characteristics and performance metrics.
3. Outlier Detection: Skill in identifying and handling outliers in the data to improve forecast accuracy.
4. Scenario Analysis: Capability to perform scenario analysis by simulating different forecast scenarios and assessing their impact on business outcomes.