|X-12-ARIMA Seasonal Adjustment Program||X-12-ARIMA is a seasonal adjustment software that was produced by the Census Bureau.
· Extensive time series modeling and model selection capabilities for linear regression models with ARIMA errors (regARIMA models);
· Many seasonal and trend filter options;
· Diagnostics of the quality and stability of the adjustments achieved under the options selected;
· The ability to efficiently process many series at once.
The X-12-ARIMA seasonal adjustment program of the US Census Bureau extracts the different components (mainly: seasonal component, trend component, outlier component and irregular component) of a monthly or quarterly time series. It is the state-of-the- art technology for seasonal adjustment used in many statistical offices. It is possible to include a moving holiday effect, a trading day effect and user-defined regressors, and additionally incorporates automatic outlier detection. The procedure makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations.
|XDATA||XDATA is developing an open source software library for big data to help overcome the challenges of effectively scaling to modern data volume and characteristics. The program is developing the tools and techniques to process and analyze large sets of imperfect, incomplete data. Its programs and publications focus on the areas of analytics, visualization, and infrastructure to efficiently fuse, analyze and disseminate these large volumes of data.|
|xgboost||Gradient Boosting (GBDT, GBRT or GBM) Library for large-scale and distributed machine learning, on single node, hadoop yarn and more.|
|xGEM||This work proposes xGEMs or manifold guided exemplars, a framework to understand black-box classifier behavior by exploring the landscape of the underlying data manifold as data points cross decision boundaries. To do so, we train an unsupervised implicit generative model — treated as a proxy to the data manifold. We summarize black-box model behavior quantitatively by perturbing data samples along the manifold. We demonstrate xGEMs’ ability to detect and quantify bias in model learning and also for understanding the changes in model behavior as training progresses.|
|X-Means||Extending K-Means with efficient estimation of the number of Clusters.|