Collaborating Authors

Online Conditional Outlier Detection in Nonstationary Time Series

AAAI Conferences

The objective of this work is to develop methods for detecting outliers in time series data. Such methods can become the key component of various monitoring and alerting systems, where an outlier may be equal to some adverse condition that needs human attention. However, real-world time series are often affected by various sources of variability present in the environment that may influence the quality of detection; they may (1) explain some of the changes in the signal that would otherwise lead to false positive detections, as well as, (2) reduce the sensitivity of the detection algorithm leading to increase in false negatives. To alleviate these problems, we propose a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation. Our experiments on several data sets in different domains show that our method provides more accurate modeling of the time series, and that it is able to significantly improve outlier detection performance.

Identify, describe, plot, and remove the outliers from the dataset with R (rstats)


In statistics, a outlier is defined as a observation which stands far away from the most of other observations. Often a outlier is present due to the measurements error. Therefore, one of the most important task in data analysis is to identify and (if is necessary) to remove the outliers. There are different methods to detect the outliers, including standard deviation approach and Tukey's method which use interquartile (IQR) range approach. In this post I will use the Tukey's method because I like that it is not dependent on distribution of data.

How To Detect Outliers In Dataset


Handle the outliers is biggest and challengeable task in Machine learning. An outlier is a data set that is distant from all other observations. A data points that lies outside the overall distribution of the dataset. Now, let understand with the help of example…. So, in salary column all employee's salaries fall under this range.

How to Handle/Detect Outliers for machine learning?


Why It is important to identify outliers? Often outliers are discarded because of their effect on the total distribution and statistical analysis of the dataset. This is certainly a good approach if the outliers are due to an error of some kind (measurement error, data corruption, etc.), however often the source of the outliers is unclear. There are many situations where occasional'extreme' events cause an outlier that is outside the usual distribution of the dataset but is a valid measurement and not due to an error. In these situations, the choice of how to deal with the outliers is not necessarily clear and the choice has a significant impact on the results of any statistical analysis done on the dataset.

Will the UK's plans to ban 'gay conversion therapy' succeed?

New Scientist

The UK government has announced plans to ban controversial and long-discredited "gay conversion therapies" that claim to help gay people become straight, and transgender people revert to the gender they were designated at birth. The move follows a nationwide survey of 108,000 LGBT people in the UK, which revealed that 2 per cent of them had undergone conversion therapy, and a further 5 per cent had been offered it. Such treatments, also known as reparative therapy, have long been denounced as pseudoscience and potentially harmful.