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Questions tagged [outlier]

For questions regarding outliers or unusual points in the data.

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Using median filter to find outliers in stock prices dataset filters out valid data

i am using next piece of code to filter outliers from stock prices data, it uses median filter to smooth data, subtracts smoothed version from original, saves it as error, and checks if error exceeds ...
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15 views

Outliner detection with LSTM autoencoder

I am learning about autoencoders for outlier detection. I have searched enough and internet suggest to use LSTM autoencoders for outlier detection from multivariant time series data. I have watched ...
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1answer
14 views

Anomaly detection in nominal big data

I have to apply an anomaly detection algorithm on big data, the values of each column on my dataframe are nominal and vary over 10000 times, the algorithms I've found only accept numeric values, is ...
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1answer
41 views

Finding outliers from multiple files [on hold]

I am dealing with a very strange problem. I have a lot of files. I need to show which files are similar and which one has exception/outliers using its data. I can show with unsupervised learning ...
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1answer
17 views

Cleaning the univariate dataset with high noise

At this time, I am having a dataset containing the operating duration for some sensors. This could be considered as a univariate dataset because it has only 1 dimension. For example: ...
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1answer
31 views

Best way to deal with realistically imbalanced dataset for Regression problem

I have a dataset where each object has a label between 0-1. Objects with label = 1 are very common but those with label = 0 are very rare. I am interested in predicting the label in unseen data. NOTE:...
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11 views

Practical examples/tutorials of using One-Class Support Vector Machines

I am a newbie in machine learning, and hope to solve an anomaly detection task using One-Class Support Vector Machines (OCSVM). ...
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1answer
27 views

Collinearity and Outlier Removal

I am playing with a credit fraud detection dataset at Kaggle. An imbalanced dataset with about 0.1% of fraud transaction. The features are 28 PCs out from a PCA exercise done by the data publisher + ...
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38 views

Outlier detection for text documents

I'm trying to build a document classifier which can encounter a lot of documents that doesn't belong to any of the pre-trained classes and these should be accurately identified as Unknown Since ...
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6 views

Leave one out for outlier remove in panel data

I'm working on a class project which involves calculating total sales for a given clothing brand from a sample retailer panel. I have monthly sales totals, and I want to estimate quarterly sales for ...
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1answer
31 views

Which order is correct Feature Selection then Outlier Detection or vice versa?

which of these orders is correct? First (Feature Selection) Second (Outlier Detection) or First (Outlier Detection) Second (Feature Selection)
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20 views

Handle outliers, Losing many data by removing natural outliers

I have 2 skewed features, here is the summary of one of the features ...
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2answers
101 views

How to use Autoencoders for outlier detection on images

I have a bunch of images taken from a camera showing a pipe and would like to detect if the pipe is leaking or not. There are very few examples of leaking pipe in the data set. So considering this ...
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21 views

Best practice for outlier removal in Investigating a process deviation

In a controlled process, in which a specific product depicted a deviation in a final product result. The process is time controlled, in which the historical manufacturing experience of the product is ...
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1answer
36 views

Is there an upper bound for k in nearest neighbors-based methods?

When applying a nearest neighbors-based method to a data of, for instance, 2000 points, what is the largest number of neighbors that can be considered ? I am using a nearest neighbors method in an ...
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1answer
25 views

How to make multiple regression perform better for outliers? (without reducing effect of them)

I have a small dataset(about 60 samples) and I need it to predict well for high target values. There are only a few high values and all models I tried perform poorly for these high values. So I ...
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2answers
41 views

Replace Values in Vector on Specific Place in R

I want to make $5^{th}$,$10^{th}$,$15^{th}$,$20^{th}$ and $25^{th}$ values of vector an outlier in all xs by using x1 [5]+OT1,...
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1answer
55 views

Probabilistic Outlier Detection (edited + clarified)

Measured data $D \in \mathbb{R}^3$, every $d^i \in D$ is $d^i_{(x)}$, where the $x=[x_1, x_2]$. Simply said, the measured data are function of $x$. It is known, the dependency is linear, such as: $$...
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35 views

Can we use doc2vec to detect outlier documents?

I have a set of documents and I want to identify and remove the outlier documents. I am just wondering if doc2vec can be used for this task. Or are there any recently evolved, promising algorithms ...
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1answer
22 views

Should we identify outliers from population prior to taking sample?

I am revising undergrad statistics course via this course, where i am learning techniques to pull out sample from population. While ensuring that sample is a decent representative of the population, ...
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33 views

Taking advatage of the frequency of an outlier

I'm doing outlier detection (Conditional Outliers) on a multivariate time series. The outliers appear every 2 weeks $\pm$ 4 days. How can I incorporate this prior in my models, to reduce the number ...
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3answers
109 views

Why replacing null values with outliers?

I have been watching a tutorial on stock price prediction with multivariate linear regression and the tutor replaces missing value data, NaN, with the outlier -99999. Why and how do replacements like ...
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1answer
1k views

Remove Local Outliers from Dataframe using pandas

Could someone please suggest how to remove local outliers from the dataframe? I have the code to detect the local outliers, but I need help removing them(setting these values to zero) in the dataframe....
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1answer
183 views

LOF gives same number of outliers irrespective of parameters

I am running lof algorithm for around 100k 2d points. Each time, I run the lof algorithm with different n_neighbours parameter, I get the same number of points as outliers. It's always 10% of the ...
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2answers
41 views

Unsupervised Clustering of 6-d vectors

I have 17k vectors, each having 6 points, I want to cluster the vectors based on nature of points, eg. linearly increasing into one cluster, convex in another cluster, concave in another, decreasing ...
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1answer
916 views

Implement Sliding Window Algorithm to Detect Spikes

How do I implement sliding window algorithm with a window size of 10 and visualize the data iteratively to see spikes/possible outliers in the dataframe, using python? Please help a beginner.
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2answers
510 views

Outlier detection with sklearn

I've been reading the sklearn documentation on outlier detection, and even the examples provided by the documentation. Once I have fitted my dataset, all I can do is apply the predict function to the ...
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2answers
286 views

Using local outlier factor score to detect outliers at run time

I am using LOF ( local Outlier factor) to detect outliers in my data. I get LOF score as outlier distance. this unsupervised learning doesnt help to detect outliers at run time. So I want to use my ...
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2answers
792 views

Handling outliers and Null values in Decision tree

Outliers : As I understand, decision trees are robust to outliers. Can anybody please confirm if my hypothesis is right with an example? (What if I have a features ranging from 0 to 9 but there is an ...
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1answer
34 views

Understanding a Box plot

I have a dataset and I have tried plotting it in a box plot after loading the csv in usings pandas. The plot itself is given below. I need to understand why the x1 ...
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1answer
74 views

Principal Component Analysis and abnormal data

I know that PCA is good in differentiating between anomalies and normal data and it helps to differentiate between them when it tries to transfer the data to another dimension. I mean it can somehow ...
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1answer
1k views

Multivariate outlier detection with isolation forest..How to detect most effective features?

I am trying to detect outliers in my data-set with 5000 observations and 800 features. I have followed the simple steps told in http://scikit-learn.org/stable/auto_examples/ensemble/...
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2answers
92 views

General methods outlier detection

What are general methods for outlier detection that do not assume any underlying distribution in the data? I have a dataset with the prizes of the rents in London, as well as their location, number of ...
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0answers
121 views

Is there any tool to remove outlier from dataset?

I have a multivariate data set and the target variable is nominal. I want to remove outliers from my dataset. Is there any tool that can help me or I have to write the code by myself?
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1answer
31 views

outlier detection in time serie without using windows [closed]

I would like to know if it's possible to detect outliers in a time-serie with an outlier score computed given the whole dataset and not given windows
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206 views

Isolation forest results every value -1

I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the ...
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2answers
912 views

When to remove outlier in preparing features for machine learning algorithm

I have a numeric variable (price) and it has long tail in both training and test data sets. I found that if you remove the highest 1% of the value in both train and test data set for this variable, ...
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124 views

Use cases for global vs. local anomaly detection algorithms

I am researching the methods available for multivariate anomaly detection is a specific context. I have played around with both global methods (e.g. one class SVM) and with local ones (e.g. LOF). Both ...
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1answer
146 views

An outlier detection for this data

I have a vector and want to detect outliers in it. I need an outlier detection method (a non-parametric method) which can detect red points as outliers. Edit: I have a lot of vectors like this. The ...
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3answers
979 views

To detect unauthorized access using outlier detection

I am working on project where my task is to find unauthorized access using any machine learning technique. Let me clear my problem definition. UserA access website using chrome browser from windows ...
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1answer
49 views

Differentiating between normal and attack clusters

In the below figure, we plotted some data of sensors in normal condition and under attack(outlier): 1. Green points are normal samples in the training dataset. 2. Cyan and red points are normal ...
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1answer
397 views

Isolation Forest height limit absent in SkLearn implementation

In the original publication of the Isolation Forest algorithm, the authors mention a height limit parameter to control the granularity of the algorithm. I did not find that explicit parameter on the ...
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4answers
204 views

How to detect anomalous points

As is clear from the figure, the blue points, which don't follow the trend, are anomalous points. I'm wondering about the best non-parametric method to detect those points. I have tested some ...
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0answers
100 views

Sensitivity analysis in outlier explanation

I am trying to find the outlier explanation using the sensitivity analysis. Let’s consider that my dataset contains 19 different input values and 1 output value (So overall 20 different columns are ...
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2answers
1k views

What is the difference between outlier detection and anomaly detection?

I would like to know the difference in terms of applications (e.g. which one is credit card fraud detection?) and in terms of used techniques. Example papers which define the task would be welcome.
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2answers
73 views

How to decide for the contamination value (proportion of the outliers) in my dataset?

I should decide on the contamination value while using the Isolation Forests algorithm (I am using the sklearn implementation). Otherwise, sklearn's default is 0.1. I am worried if I decide for this ...
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0answers
56 views

Outlier detection: Should the metric used in kNN take into account variance explained by each coordinate?

After applying PCA and working with the reduced dataset, I want to delete the outliers. To do this my idea is to compute the kNN-graph and delete those vertices (points) that have an inner degree of 0....
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2answers
225 views

How to increase the weight when it comes to outlier detection

Let's say we have feature A, B, C, D, E to represent one observation in an outlier detection model. We are using scikit-learn outlier detection in our case. AFAIK, if we normalize all the features, ...
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2answers
615 views

Anomaly detection in time series

The use case : Everyday, we have metrics that are established daily to check that our systems are doing fine. From time to times, bugs occur in the workflow building these metrics, and I have to ...
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1answer
2k views

Can the Generative Adversarial Network useful for Outlier detection and Outlier explanation in a high dimentional numerical data?

I have been building a model to find explanation of Outliers in a high dimensional numerical data, generated from many sensors. The data contains more than 350 different fields and each field has ...