Time series data is widely used in various fields, such as finance, economics, and engineering. One of the key challenges when working with time series data is detecting level shifts. A level shift occurs when the time series’ mean and/or variance changes abruptly. These shifts can significantly impact the analysis and forecasting of the time series and must be detected and handled properly.
Tag: forecasting
It is said that everything is big in Texas, and that includes big data. During my recent trip to Austin I had the privilege of being a judge in the final round of the Texata Big Data World Championship, a fantastic example of big data competitions. It felt fitting that
The date of Easter influences our leisure activities Different from many other public holidays, Easter is a so-called movable holiday. This means that the Easter bunny brings more than just eggs for the statistician - he brings special Easter forecasting challenges. In the year 325 CE the Council for Nicea
This post will violate the “what happens in Vegas stays in Vegas” rule, because last week I had the pleasure of attending and participating in the Analytics 2014 event there and want to share some of what I heard for those who couldn’t attend. I was joined by over 1,000
It is a mild summer evening in July at Lake Neusiedl here in Austria. The participants of the traditional YES Cup Regatta are sitting with beer and barbecue chops on the terrace of our clubhouse. The mood is relaxed, and everyone wants to tell their story after two eventful races.
My esteemed colleague and recently-published author Jared Dean shared some thoughts on how ensemble models help make better predictions. For predictive modeling, Jared explains the value of two main forms of ensembles --bagging and boosting. It should not be surprising that the idea of combining predictions from more than one