SAS Forecast Server Reviews & Pricing

Key Features

• Easy to Use GUI: It includes SAS Forecast Studio GUI, which provides users with easy access to SAS forecasting capabilities, without any knowledge of SAS programming. Also, the SAS programming language can allow advanced users to get additional forecasting power. The new project wizard helps users generate automatic forecasts in batches or interactively. 
• Hierarchical Forecast Reconciliation: It can model and forecast every series in the hierarchy individually, then reconcile forecasts across the full hierarchy according to the user’s choice of top-down, bottom-up or middle-out reconciliation. 
• Scenario Analyzer: SAS Forecast Server lets planners test what-if scenarios like changes to pricing and promotions. A scenario analyzer can be used to determine the likely effect of these changes on future demand. 
• Scalability and Modeling: It can facilitate ongoing and repeatable forecasting throughout the organization. An extensible model repository can help users to create appropriate forecasting models for a wide range of behaviors. It can enhance performance through multithreading of the forecasting and diagnostic engines. 
• Easy Manageability: SAS Forecast Server can automatically identify outliers and shifts in data by examining the history of each item being forecast. It helps adjust subsequent forecasts appropriately. Forecasters can incorporate individual judgement or outside information using the manual override console that includes override locking. 
• Interactive Time Series Exploration: It helps explore and analyze large volumes of time series data prior to forecasting using a graphical user interface. Data problems like missing values and date ID issues, outlier detection and management can be easily identified. It allows time series characterization, data segmentation into subsets and structural manipulation of collective time series. 
• Automatic Forecasting: SAS Forecast Server can determine the most suitable forecasting models for historical data. For each item being forecast, an appropriate model can be generated based on user-defined criteria. It can automatically optimize model parameters. 

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