ARMA/ARCH/GARCH parameter estimates are biased by The magnitude if the change point jumps. Larger jumps will lead to lower estimated AR. The proportion of change points relative to the number of ...

If the structural break only affect a subset of the model parameters, you could use the whole time series to model all other parameters (carrying over info) and only use post-break info to infer the ...

Here's a solution using the R package mcp. Say we simplify your problem as a plateau followed by an abrupt change to a negative slope, followed by a plateau. Let's simulate some data: data = data....

The R package bcp seem to fulfill all of these (associated paper here). It returns the probability of change point at each index in your data, so you have to set a threshold yourself. This is a nice ...

Given that the samples are independent, I would Infer the posterior of the binomial rate in each sample, e.g., $p_A n_A \sim Binomial(n_A, p_A)$. Compute the posterior distribution for ($\delta_{AB} =... View answer 1 votes The Bayesian framework is apt for including prior information and giving you prediction intervals. The mcp package can model AR(N) time series and the docs has a section on forecasting with future ... View answer Accepted answer 1 votes Classical statistical process control (SPC) operates only operates on a single sequence of data points (ranked time) without taking into account their temporal distance. That is, it does not matter ... View answer Accepted answer 2 votes It makes a difference whether you want to maximize the predictive accuracy of future data or infer parameters from at-hand data. Predicting future data If your goal is out-of-sample prediction, then ... View answer 0 votes The ranger package supports quantile predictions and hence prediction intervals: predict(ranger_fit, type = "quantile", quantiles = c(0.025, 0.975)). View answer 0 votes There are many R packages that can do bernoulli/binomial discontinuity regression out of the box (they are identical for binary outcomes). See an overview here. The R package mcp with mcp(model, data, ... View answer 0 votes This ressource lists most of the R packages available for change point analyses. A good handful of them can model AR(N) models which is a Markov process. In the AR(N) literature the term "non-... View answer Accepted answer 3 votes If you are open to using R, here is a solution using mcp. mcp can infer the location of changes in means (worked examples), variances (worked example), autocorrelation (worked example), and any ... View answer 1 votes If you're open to using R, this would be the mcp model: model = list( y ~ 1, # b ~ 0 + m, # b + add slope ~ 0 # flat line from here ) fit = mcp(model, data) mcp will estimate the common ... View answer 3 votes If you want a probabilistic inference on the location of$k\$ (the change point), mcp is well suited for cases like this. It infers the parameters of change point models using Bayesian Inference (see ...

Bayesian inference is as coherent way to include prior knowledge. To my knowledge, only the mcp package allows for setting priors on change points. There are several approaches to your model ...

If your data is distributed in a well-defined way, Westgard rules are probably the simplest solution. For your problem, I'd propose something like this: Compute some lower bound on "good" periods in ...

There are many R packages that can fit ARIMA models to break-point data. I compiled an overview of some of them here: https://lindeloev.github.io/mcp/articles/packages.html. Fewer of these can do a ...

Change points can be difficult in a frequentist framework. Most methods involve identifying the change point as a fixed location, ignoring the uncertainty inherent in estimating it. In addition, ...

You have prior knowledge that the breakpoint can only occur at values between 5 and 20. Therefore, Bayesian modeling is well suited to this task. One further advantage of going Bayesian is that you ...

There are a lot of R packages. Here is a non-exhaustive overview with worked examples. Below, I'll show a solution using the mcp package. Let's start by simulating some data that look a bit like ...

It seems that you are really modeling intervals between events. From your description it sounds like the trends do not follow smooth periods (Fourier) or autoregressive patterns. So the model could be:...

Here is a solution using the mcp package. You specify the regression model on a segment-by-segment basis. Model Let's say that the segments all have an intercept and a slope. Also, for the sake of ...

If I read your question correctly, you want to infer the distances between the plateau heights and the associated uncertainty of this inference. If it makes sense to think of this as a change point ...

The R package mcp uses JAGS to do do regression with change points, and it includes the option do have per-subject change points. Read more in the article on varying change points in mcp. See my ...

Some more information is needed to figure out the best solution here, so I'm simply answering a number of scenarios with example R code. Modeling the outcome If the outcome is binary, use family = ...

You can do this with mcp. First, let's get your data in an accessible format. The variable "days" is the number of days since the first record. I remove the NAs: library(dplyr) D = read.table("...

I made the R package mcp exactly because there is a lack of packages quantifying the uncertainty (e.g., SE) about the inferred change point locations. Change point problems are conceptually simple in ...

You can do this in the R package mcp. Although your actual full model may be outside the scope of mcp, this is a way to do "random effects" change points. The mcp package contains a demo dataset ...