Animated sequential trend signal detection in finite samples

In this paper we describe an animated method which may allow for the detection of the trend signal, independently of any subjectivism caused by the length of the time series. The new algorithm decomposes the series into a succession of sequentially tendencies, having the feature of sloped steps, in the biggest having divergence shape related to the horizontal that reflects the global mean of the given data sample and the time flow. The sequential trend signal turns out to be more adequate as far as the statistically explained variance is concerned. The presented animation program allows the user to view the creation process for this signal in real time according to new data inserted simultaneously.