Data manipulation capabilities that include functions for summary tables generation, data split, concatenation, transposition, stacking, sorting, flexible filtering, joining tables, and so on.

       Missing data handling that includes different options to impute missing data.

       Data description capabilities that are usually based on basic descriptive statistics, frequency tables, histograms, and so on.

       Data visualization capabilities that include a broad spectrum of graphics, such as 3-D scatter plots, contour plots, parallel plots, and so on.

       Data pre-processing capabilities that include filtering, outlier detection and removal, data sampling, data partitioning, data transformation, and so on.

       Basic statistical capabilities that include building and analyzing linear regression models with options for variable selection by forward and backward stepwise regression.

       Multivariate analysis capabilities that include cross-correlation analysis, PCA, and PLS.

       Clustering capabilities that include dividing variables in clusters by linear or nonlinear methods, similarity analysis, and building decision trees.

       Variable selection capabilities that include different algorithms for variable selection, such as stepwise regression, decision trees, gradient boosting, singular value decomposition (SVD), and so on.

       Time series analysis capabilities that include generating time series, different time plots, correlations, seasonality adjustments, decompositions, and so on.

       Forecasting model generation capabilities that include the most popular methods, such as exponential smoothing, ARIMA, unobserved components, and so on with a variety of diagnostic statistics and model performance metrics.

       Forecasting modeling with events capabilities that enable the introduction of big discrete shifts in the model development.

       Hierarchical forecasting capabilities that include developing a model hierarchy at the desired level based on the existing business structure and reconciling this with the final forecast.

       Scenario generation capabilities for multivariate-based forecasting models—these different “what if” scenarios can show the impact of the key inputs on the final forecast.

       Cost depends mostly on the ease-of-use of the corresponding packages. Most of the time the tools based on building blocks (such as SAS Enterprise Miner) or the high-performance forecasting tools (such as SAS Forecast Server) cost more. However, the increased productivity they deliver is significant. An additional advantage is the shorter learning and product adaptation time, which lowers the total cost.

       Functionality—it is strongly recommended that you carefully check whether the necessary technical functionality is available, as described in the previous sections, and to avoid any compromises. The capability to add new methods is also recommended.

       Ease-of-use is enhanced by programming based on building blocks, a high level of automation for data pre-processing and model generation, an interactive graphic interface, and minimal programming necessary to deploy models (all features of SAS Enterprise Guide, for example).

       Report generation is a significant step during model development as well as during model deployment and when transferring ownership to clients. During the model building phase many detailed reports with time series analysis, model diagnostics, or variable selection results are needed for successful decision-making. For model deployment, good reporting capabilities for model performance and value tracking are critical in order to keep the client happy.

       The learning effort required depends on the software's ease-of-use, users' experience in statistics and forecasting within the organization, and the training courses and materials offered by the vendor. Products with a steep learning curve can significantly delay implementation efforts and reduce the impact of the technology for data mining in forecasting.

       Global support 24/7—a fast, professional response to model development and implementation issues that is available globally is critical for the success of data mining for forecasting in industry. This is one of the key factors to consider when selecting the proper software vendor. Very few have the capacity to provide this type of service.