{"id":148361,"date":"2006-07-01T00:00:00","date_gmt":"2006-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mining-cross-predicting-stochastic-arma-time-series-in-sql-server-2005\/"},"modified":"2018-10-16T21:08:49","modified_gmt":"2018-10-17T04:08:49","slug":"mining-cross-predicting-stochastic-arma-time-series-in-sql-server-2005","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mining-cross-predicting-stochastic-arma-time-series-in-sql-server-2005\/","title":{"rendered":"Mining cross-predicting stochastic ARMA time series in SQL server 2005"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present a prototype that we have developed for analyzing so-called stochastic ARMA models in SQL Server 2005, Analysis Services. The class of stochastic ARMA models extends the classic ARMA time-series models by replacing (or smoothing) the deterministic relationship between target and regressors in these models with a conditional Gaussian distribution having a small controllable variance. As this variance approaches zero, a stochastic ARMA model approaches a classic ARMA model. We represent a stochastic ARMA model as a directed graphical model. In doing so, we benefit from the ability to apply standard graphical-model-inference algorithms during parameter estimation (including estimation in the presence of time series with incomplete data), model selection, and prediction. The graphical model representation also offers a visual representation that is easy to interpretate. We demonstrate how the graphical representation in this way lends itself as a conceptually easy way of extending the models to handling cross predicting time series, periodicity, and trends.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a prototype that we have developed for analyzing so-called stochastic ARMA models in SQL Server 2005, Analysis Services. The class of stochastic ARMA models extends the classic ARMA time-series models by replacing (or smoothing) the deterministic relationship between target and regressors in these models with a conditional Gaussian distribution having a small controllable [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"thiesson","user_id":"34026"},{"type":"user_nicename","value":"jesperl","user_id":"32242"}],"msr_publishername":"WIT Press","msr_publisher_other":"","msr_booktitle":"Data Mining VII: Data, Text and Web Mining and their Business Applications. Information and Communication Technologies","msr_chapter":"","msr_edition":"Data Mining VII: Data, Text and Web Mining and their Business Applications. 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