Questions tagged [anomaly-detection]

Question 1

I'm working with a dataset consisting of multiple CSV files, each representing time series data of accelerations (x, y, z) captured during vibration events. For each event, a sensor records data for ...

Question 2

I am working on a project where I am doing Unsupervised Anomaly Detection on employee expenses on HCP transfer Of Value. I am trying to use Graph Neural Network to detect anomalies with proper ...

Question 3

I am using a rolling window z-score method to flag if a record is an outlier. Is it necessary to first normalize the values of the desired feature before computing the rolling z-score?

Question 4

I am looking into different statistical methods for determining a decrease in a numeric "count" feature across a time-series dataset. The dataset is relatively small (about 50 records), and ...

Question 5

Anyone know why PyGOD's DOMINANT implementation produces a memory error even though the batch size argument is reasonable? To reproduce: ...

Question 6

I am using sklearn's IsolationForest for unsupervised anomaly detection task. According to the docs, https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html, there are ...

Question 7

I am looking to better understand sklearn IsolationForest decision_function. My understanding is that if the metric is closer to -1 then the model is more confident ...

Question 8

I have implemented an Isolation Forest algorithm for anomaly detection (unsupervised learning), where I divided my dataset into 1000 subsets, and for each subset, there is one isolation tree. This ...

Question 9

I’m working on a large dataset (300+ columns, 500k+ rows) and have been asked to build an anomaly detection algorithm, but I’m unsure how to define or approach these anomalies in a meaningful way. ...

Question 10

TLDR Have background in MLOps and machine learning engineering, started at a new employer (as the first AI engineer) and failed in a project of time series forecasting. Approach detailed below, any ...

Question 11

As far as I know, tree models (such as those trained using xgboost/lightgbm) makes reasonable prediction only if the input feature vector is similar to the train set data. If the feature vector looks ...

Question 12

I hope someone can help me with a work problem I am facing. My data has machineID, timestamp(UTC), batterypotential for multiple machines over 14 days for every 2 mins. I need to look at their time ...

Question 13

I'm working on anomaly detection in timeseries data, and need to add synthetic anomalies to existing timeseries data (in order to test anomaly detection algorithms). I can do this by running a ...

Question 14

Dataset Overview I have a dataset with three columns: ProjectCode: A categorical variable representing the project. (~6 unique values per category) ...

Question 15

I'm working on an anomaly detection problem for motor experiments 9same type of motor) and need advice on statistical approaches (No ML since I do no not have enough data). Here's the context: Dataset:...

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Questions tagged [anomaly-detection]

Unsupervised anomaly vibration detection for time series

GNN Based unsupervised Anomaly Detection for Heterogeneous Graphs

Rolling z-score and normalizing

Anomaly detection time in time-series for drops

PyGOD memory error despite batch size argument

Unsupervised Isolation Forrest sklearn hyperparameters

Confirm understanding of decision_function in Isolation Forest sklearn

Determine best hyperprameteres in GridSearch - Isolation Forest

What are the Strategies for Anomaly Detection in Sparse Datasets?

How Would You Approach This Project on Time Series and Anomaly Detection

How to determine the feasible domain of a trained tree model?

Battery Disconnect Categorization

Are there any libraries for generating synthetic anomalies in timeseries data in python?

How to Handle Predictions with Two High-Cardinality Categorical Variables?

Statistical Approach for Anomaly Detection in Multivariate time series

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