## 1 Introduction  
Outlier detection, sometimes referred to as novelty detection, is the process of finding out what is different from normal data. According to [PERSON], \"outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data mining and statistics literature\" [1].  
Outlier detection has been an important field of research for industry and academia. By identifying outliers, researchers can obtain vital knowledge that assists in making better decisions or avoiding risks. So, outlier detection is widely used in many fields, such as network intrusion detection [2; 3; 4; 5], intelligent transportation [6; 7; 8; 9], video content analysis and detection [10; 11; 12], fraud detection [13; 14; 15], social media analysis [16; 17; 18; 19], and data generation [20; 21].  
Over the past few decades, many outlier detection algorithms have been proposed [22; 23; 24; 25; 20]; depending on whether labeled data are utilized, they can be divided into three main categories: (1) supervised methods, (2) semi-supervised methods, and (3) unsupervised methods. We will provide more details on these methods in Section 2.  
While these algorithms were shown to be effective in earlier applications, as the concept of big data has become more prevalent and data have become more multidimensional, they have increasingly become more problematic.  
(1) Missing labeled data. Supervised algorithms require a large amount of labeled data that, in many cases, are difficult to implement or require incurring high costs. This can lead to unsatisfactory performance being demonstrated by these supervised algorithms.  
(2) Curse of Dimensionality. In the era of big data, the dimensionality of data is increasing. The performance of supervised outlier detection algorithms, especially those based on proximity, will decrease rapidly with the increasing data dimensionality.  
(3) Interpretability. In practical applications of anomaly detection, such as credit card fraud detection and medical imaging inspection for anomalies, we not only need to be able to detect anomalous data but also need to make a reasonable explanation as to why these data are anomalous. Due to the disparity in the distribution of outliers and normal instances, the peculiarities of various detection algorithms, and the complexity of data structures in particular applications, it might be challenging to explain abnormalities in outliers.  
To avoid the above shortcomings, this paper proposes a new outlier detection algorithm based on quantiles and skewness coefficients: Interpretable Single dimension Outlier Detection (abbreviated as ISOD). In this method, the empirical cumulative distribution function of each sample's dimension is first determined using data from that sample; the skewness coefficient and quantile of the empirical cumulative distribution function are then computed. Finally, the skewness coefficient is used as a weight to summarize the anomaly score of that data point so that anomaly detection results can be obtained.  
The rest of this paper is organized as follows: In Section 2, an overview of the current anomaly detection algorithms is provided. In Section 3, the focus is on the proposed algorithm (ISOD) and its analysis. In Section 4, the experiments we conduced and their results are analyzed, and Section 5 concludes the article.