Time Series Analysis Using Max/Min… and some Neuroscience.

Introduction Time series has maximum and minimum points as general patterns. Sometimes the noise present on it causes problems to spot general behavior. In this post, we will smooth time series -reducing noise- to maximize the story that data has to tell us. And then, an easy formula will be applied to find and plot max/min points thus characterize data. What we have # reading data sources, 2 time series t1=read.csv(“ts_1.txt”) t2=read.csv(“ts_2.txt”) # plotting… plot(t1$ts1, type = ‘l’) plot(t2$ts2, type = ‘l’) As you can see there are many peaks, but intuitively you can imagine a more smoother line crossing in the middle of the points. This can achieved by applying a Seasonal Trend Decomposition (STL). Smoothing the series # first create the time series object, with frequency = 50,…

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