Questions tagged [concept-drift]

In the context of data streams concept drift describes the phenomenon of the underlying distribution of a variable changing over time, often negatively affecting the performance of statistical models trained on perviously observed data.

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What is the corresponding model when I include drift in an SARIMA model with seasonal differences?

I fitted the following model, but I am not sure if the drift term is an intercept term or the mean of $y_t-y_{t-4}$ (as it seems in a blog post by Rob Hyndman). ...
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Is this an example of overfitting?

I am trying to predict some future values using either KNN or regression model. I have about 9 independent variables that do not seem to have strong correlation to each other (Not completely sure ...
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Are ensemble learning methods for data streams restricted to online or batch learning?

Recently I'm working on some online learning algorithm (using RBF neural network ) for classification. As I read papers in this area I found there is an issue in online-learning called concept drift ...
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Unsupervised concept drift detection in clustering

I am implementing a clustering-based algorithm for non-stationary data stream. Most concept drift techniques are based on change in classification output (or on its accuracy). Is their a way for ...
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Optimization with concept drift, changing location of the optimum over time

Is there any research/literature on the topic of optimization where the location of the optimum changes over time? Nonetheless I am interested in an optimal solution at any given time. It's hard for ...
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lifetime of fraud detection models

Suppose we are building/testing a fraud detection model for a specific credit card/ or a quick cash loan business. We have a lot of data to play with (say past 5years), and after careful preprocessing,...
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Is there a way to adapt machine learning models knowing ex ante that distributions will shift?

I am currently working on a topic where I know that the distributions of the output and of the covariates will shift. I know for example that some covariates will at least follow the inflation rate. ...
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Machine learning or statistical models that account for time evolution and underlying system changes

I wonder if there are some algorithms that can account for underlying system dynamics over time. One possible situation can be the following: in a ticket reporting data, a data point arrives when a ...
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How do the terms non-stationarity, concept-drift and evolving data relate to each other?

I often see the terms non-stationarity, concept-drift and evolving data in the same context, as if they were interchangeable. Are they? Or is there some subtle nuance that I am missing?
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Synthetic datasets for concept drifting data

Is there any synthetic / artificial datasets for concept drifting data? I want to visualize the performance of some clustering algorithm when data experiences concept drift and changes over time.
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concept drift detection

I'm working on a project that involves concept drift detection for a time series. Are there any well-known techniques/methods/algorithms that are known to be effective for this sort of problem? ...
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Statistics tests for monitor changes in data distribution (concept drift) without knowing real output

I'm currently struggling with concept drift problem in on-line learing. I read some papers "Ikonomovska Gama - Regression Trees from Data Streams with Drift Detection " and check their implementation ...
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Benchmark Data-sets for Concept Drift where important predictors (independent variables) change with time or stream of observations [closed]

I'm currently searching the web and literature for streaming classification datasets with concept drift. I've found a number of synthetic datasets where over time the important predictors either ...