I've attached a picture of the time series I'm talking about. The top is the original series, the bottom is the differenced series. Each data point is a 5 minute average reading from a strain gauge. This strain gauge is placed on a machine. The noisy areas correspond to areas where the machine is turned … Read more
I've got some time-series business data that I can fit relatively well with a ARIMA(2,1,0)(1,1,0)[12] model (using R's excellent forecast::Arima — thanks Prof. Hyndman!). The series is dominated by seasonal effects, but has trends as well, thus the differencing. I'm not an expert in forecasting. I'm exploring a future experiment (power analysis sorts of things) … Read more
I am testing the R package changepoint, and the results are strange. Here is my code: x = 1:100 x = x + rnorm(100, 0, 10) cpt1 <- cpt.mean(x, test.stat="Normal", method="PELT") x = 1:100/10 x = x + rnorm(100, 0, 1) cpt2 <- cpt.mean(x, test.stat="Normal", method="PELT") par(mfcol=c(2,1)) plot(cpt1) plot(cpt2) I don't think this makes any … Read more
I am testing the R package changepoint, and the results are strange. Here is my code: x = 1:100 x = x + rnorm(100, 0, 10) cpt1 <- cpt.mean(x, test.stat="Normal", method="PELT") x = 1:100/10 x = x + rnorm(100, 0, 1) cpt2 <- cpt.mean(x, test.stat="Normal", method="PELT") par(mfcol=c(2,1)) plot(cpt1) plot(cpt2) I don't think this makes any … Read more
I have the following dataset: results <- data.frame(Date = c("A", "B", "C", "D", "E", "F", "G", "H","I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S"), P1 = c(0.43, 0.45, 0.57, 0.15, 0.5, 0.33, 0.26, 0.81, 0.43, 0.48, 0.14, 0.26,-0.21, 0.27, 0.37, 0.33, 0.68, 0.15, 0.44)) I want to know, if there are statistically … Read more
There are quite a few algorithms to detect changepoints, outliers, mean shifts, trend shifts etc. out there. Recently I've stumbled upon BreakoutDetection and while it's new and shiny I'd like to know if there are any problems with using the algorithm to detect, specifically, multiple mean shifts in non-normally distributed random processes (e.g. studies reviewing … Read more
I'm trying to code the changepoint detection algo described here: Link to original ppt: http://www.slideshare.net/kuma0177/velocity-ny-2014v5-39160794 Slides: 13-16 Slide 16: http://imgur.com/fF69Q3Q It is my understanding that the slides use a rolling median as input to calculate lambda on slide 16. However, I'm not entirely sure how you plug the median into that lambda equation. My stats … Read more
I'm trying to code the changepoint detection algo described here: Link to original ppt: http://www.slideshare.net/kuma0177/velocity-ny-2014v5-39160794 Slides: 13-16 Slide 16: http://imgur.com/fF69Q3Q It is my understanding that the slides use a rolling median as input to calculate lambda on slide 16. However, I'm not entirely sure how you plug the median into that lambda equation. My stats … Read more
I'm trying to implement a "change point" analysis, or a multiphase regression using nls() in R. Here's some fake data I've made. The formula I want to use to fit the data is: $y = beta_0 + beta_1x + beta_2max(0,x-delta)$ What this is supposed to do is fit the data up to a certain point … Read more
I have a model that has the following characteristics: The covariate $X$ follows a $Be(1/3)$. If $X=0$, survival time $Y$ follows an $E=Exponential (1)$. If $X=1$, survival time $Y$ is generated as $E$ if $ElePsi$, and as $Psi+E_lambda$ if $E>Psi$, where $Psi$ is the 'change point' (let $Psi=1$) and $E_lambda=Exponential(lambda)$ independent from $E$. Does anyone … Read more