Methods that attempt to detect when a change occurs in a distribution, process, or function.
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7 views
Construct Confidence Curve in R in Change Point analysis
I am trying to reproduce the journal article "Confidence distributions for change-points and regime shifts" (on page 16 top left hand corner)
Firstly, I generated random sample using i) N(1,1) and ii)...
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0answers
20 views
regression change point model
I am novice at best with linear regression models and I am interested to learn how to do a change point model in Python if possible. The data that I analyze is a years worth of electrical energy (kWh) ...
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2answers
33 views
Detecting change in p of a Bernoulli process
A machine outputs either a 0 or a 1 each second. We denote this output at time $t$ as $b_t$. The probability that it outputs 1 is $p_t$ at time $t$. How do we go about studying the change in $p_t$ in $...
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0answers
13 views
Chow test with multivariate seasonal predictors
I have historic time series of daily coastal temperatures that have no accompanying meta data wrt. changing sensor type, site locations etc. I have detected change points that I would like to flag as ...
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0answers
19 views
Measuring the effect of an outside force on time series with trend and seasonality
Let's say I have a time series that shows daily traffic to my website. My website is getting more popular so there's a trend up, and the traffic is based on day of week so it's cyclical with a period ...
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0answers
31 views
How to find the timepoint when on average each timeseries reaches a given threshold with hypothesis testing?
I have N individual timeseries and would like test statistically - after the fact - when they cross a given threshold - specifically when they are reliably above 0. I am new to timeseries analysis and ...
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1answer
81 views
Pettitt's Test for Change-Point Detection Showing P-Value Larger than 1
I am studying how to use the pettitt.test function from the trend package in R to detect change-point in a time-series. However, ...
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1answer
197 views
Changepoint/Step Detection in Univariate Time Series
As a beginner to time series analysis, I'm trying to understand the best way of detecting the points at which my univariate time series shows a change in trend direction (see highlighted example).
I ...
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0answers
65 views
CUSUM algorithm and first derivative
I have a doubt about implementation of CUSUM algorithm. I found a Python implementation here.
It is based on a formulation of the CUSUM that basically tracks positive ($g^+$) as well as negative ($g^...
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1answer
94 views
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0answers
57 views
Hierarchical Version of Bayesian Change Detection Model in JAGS
I am trying to create a hierarchical changepoint detection model in JAGS, estimating group difference in changepoint based on individual changepoints in scores for an outcome variable (fictional in ...
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2answers
61 views
R changepoint, different number of outputs
I tried detect change points in R. I've ran following MWE in R.
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1answer
55 views
Finding statistically significant changes in a time series
What would be the proper way to determine statistically significant changes between time periods within a time series (between Yn and Yn+1)? I thought about taking the first difference and ...
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0answers
23 views
plotting the regression lines in each iteration (chngpt package) [closed]
I want to plot the regression lines for each iterations of the chngpt package. Link to chngpt package
Here's my sample data:
...
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0answers
204 views
Change detection point with python
How reliable is using KS test (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html) for change-point detection (in a single vector)?
Is there a better way for this, in ...
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0answers
30 views
Time series clustering/segmentation based on pattern
I'm currently working with a database which contains several large PPG (pulse oximetry) and ECG time series. These series, however, contain segments within them which are highly contaminated by noise, ...
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0answers
27 views
Finding the likelihood of a normal distribution in a change point model
The simple change-point problem can be described as follows. Here it is assumed that both $p_1(y)$ and $p_2(y)$ are known completely.
$y_1,...,y_\tau|\tau $ are iid with distribution $p_1(y)$ and $y_{...
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0answers
152 views
Edge detection in time series
I have a time series (data here) which contains several square-wave jumps, as well as some physical signals of interest. An example is shown in the top panel of the figure below. There are square wave ...
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0answers
24 views
On the use of running total instead of raw data in change point detection
I am working with a percentage time series data recorded at irregular time step that generally shows or expected to show a decreasing trend with time (and at an increasing rate towards end of the time ...
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0answers
29 views
Understanding a cost function
I have been analyzing a cost function used in a point of change algorithm.
I am having trouble understanding the notion of a cost function and how it generates results for example.
...
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0answers
38 views
Find a “drop” in continuous time-series data
I have a dataset of a lot of time-series data of single observations. I am looking for a way to find a specific pattern in this data. As seen in the figure, the data does not change much over time, ...
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0answers
35 views
Hypothesis testing on dependent samples to identify change point
I want to perform hypothesis tests on the correlated observations. Practically speaking, suppose there is a sequence of data points $X_n$, which could be regarded as the observations from a certain ...
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0answers
34 views
How to estimate point of jump in a regression model?
So non-parametrically it is observed that once the temperature hits approximately 300K, there is a jump in conductivity of a material. Now the estimate is noisy and seems to shift slightly based on ...
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1answer
119 views
Finding changepoints in the movement of a car with respect to a lead car (using ecp package in R)
Main Question:
How do I apply e.divisive method from ecp package effectively on multivariate time-series, so that the resulting ...
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1answer
183 views
Need advice on change point (step) detection
I have a time series with lots of steps/jumps (data file here). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to ...
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68 views
Detecting changes in large number of time-series that share seasonality
I have large number of time-series that are independent of each other, but share some seasonality patterns. I need to detect anomalies/changes (increased volume, change in mean), that appear in the ...
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1answer
48 views
sequences of different length
I have two sequences of coin tosses, which have different lengths. I want to compare their likelihoods under a model. But since the likelihood is a product of probabilities, the longer sequence ends ...
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1answer
33 views
Bayesian Information Criterion - Non-physical model selection
Following the work of Bai & Perron (1998) and others in detecting structural changes in time series, I am trying to select the breakpoints by using the Bayesian Information Criterion.
Basically, ...
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2answers
45 views
Demonstrate change of predictor variable for a time series [duplicate]
I have the following problem:
Assume there are three time series $h(t)$, $g(t)$ and $f(t)$.
The hypothesis is that prior to $t_1$: $h(t)=a\cdot f(t-t_2)$
and after $t_1$: $h(t)=b \cdot g(t-t_3)$.
...
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0answers
54 views
Is it possible to use statistics to detect a rising temperature scenario?
It is required to monitor temperature data from a manufacturing environment. The particular use case is to detect if the temperature is rising beyond normal values to enable timely corrective action. ...
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1answer
365 views
Identify two major change points in time series data and summarise for several replicates in R
I'm working with temporal simulations (forward in time) and I would like to find the best way to detect the two major change points in this time series.
I have several replicates of the same ...
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1answer
96 views
How do I do a change point analysis on a sparse data set in python?
So, I have some data from video game playtests, where players were allowed to play a game at home for a week, and were asked to fill out a daily survey. In particular, they were asked to rate a ...
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1answer
47 views
Time series random chance detection
I have a time series data of monthly sales for the last 4 years. The sales for one month eg Feb 2017 is much higher same month last year or the last month sales of Jan 2017.
If i wanted to detect if ...
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0answers
57 views
How to statistically demonstrate a change of the predictor variable to a responding variable from time t on (time series)?
I have the following question:
I have three time series. Two form possible predictors (f(x) and g(x)) and a third is the responding function (h(x)). f(x) and g(x) exhibit different lags to h(x). Now I ...
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2answers
4k views
Time series anomaly detection
I am tasked to develop an anomaly detection system for data organised in many 1D (can be more than 1D if I choose, but I think that will complicate the problem even more) daily time series. The series ...
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4answers
1k views
Anchoring a linear regression to a specific data point in r
This is different, follow up question to someone else's question here:
I am very new to R and have no programming experience what so ever but I am holding my own for the data analysis we need in my ...
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0answers
91 views
Changepoint detection in a single unevenly spaced time series
I have a single time series, unevenly spaced. I would like to perform change point detection on it. Does it mean a non-parametric method is needed? Any pointers to which method would be valid? Also, ...
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0answers
147 views
How to detect distribution changes within population?
I have a time series data of populations, e.g. for each time point, I have the blood pressure of 1 million of rats. How can I monitor that the distribution of this population drift over time?
For ...
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1answer
167 views
When testing for structural changes, should a linear trend be identified from the data or the derivative of data?
There exists a linear trend in an otherwise non-linear set of time-series data. The linear trend can be identified using a change-point (or breakpoint for structural changes) analysis method. This ...
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0answers
119 views
detecting change point in the time series on a 2 dimensional space
I have a 2 dimensional geographic space. There are crime events occuring at different regions in the space over time. I am looking particularly at property crimes like burglary. If I look at the time ...
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2answers
112 views
How do I split a vector and minimize absolute error efficiently?
I have a vector $y\in\mathbb{R}^n$. I want to find a computationally efficient way to find a split point $k$, such that:
$\sum_1^k |y_i-\theta_1|+\sum_{k+1}^n |y_i-\theta_2|$ is minimised. The $\...
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3answers
296 views
Forecast time series with breakpoint using Holt-Winters (R)
I have a time series dataset for forecast using Holt-winters model, but my problem is the time series contains breaks or change point,
Should we remove the first break? Or is there any other solution ...
9
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1answer
212 views
How can I highlight noisy patches in a time series?
I have lots of time series data - water levels and velocities vs time. It is the output from a hydraulic model simulation. As part of the review process to confirm that the model is performing as ...
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0answers
307 views
What exactly is Beta in the Twitter Breakout Detection Algorithm?
I have been going through the R implementation of the EDM algorithm, Twitter Breakout Detection (here) and also the corresponding paper (here) and I'm not really understanding the concept of 'Beta' ...
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1answer
224 views
Judging flatness of time-series
I have multiple short (say, length <100 points) time-series as exemplified below. All the series are made of values measured in the same units. I need to find some criterion for judging their "...
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0answers
100 views
Forecasting in R and accounting for historical events (such as product price changes)
I'm trying to use the Holt-Winters model included with the R package 'forecast' to forecast a product's sales revenues, which includes seasonality.
In the past the product's price as changed, and ...
3
votes
1answer
2k views
simple trend analysis, time series
For purposes of a non-technical analysis of a simple time-series, what is a quick (but effective) technique / approach to understand (or even 'conclude' with some confidence level) that a trend has ...
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0answers
93 views
Transforming time series data before change point analysis in R
I have count data (non-financial) from 2010-2014 by week. I am interested in using R and changepoint package to find any significant points of time when the trend changed. I have two questions about ...
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1answer
185 views
Comparing the slopes of two regression lines where the two slopes are NOT independent (breakpoint)
so I am trying to see whether there is a breakpoint in my data set at a certain time. Basically I have 200 data points and am wondering if there is a breakpoint at t=100, ie whether the slopes of the ...
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0answers
83 views
Updating sufficient statistics parameter sets in Bayesian Inference (Changepoint Detection)
I am trying to implement and customize a changepoint detection method based on Bayesian Inference (referring to https://arxiv.org/pdf/0710.3742v1.pdf). Now I struggle understanding the conjugate prior ...
