This table identifies all state-level causes of death that were at least five times the national rate in at least one of the periods 1999-2003, 2004-2008, and 2009-2013. Data are based on the 113 Cause of Death list and are based on the CDC's Underlying Cause of Death file accessible at: http://wonder.cdc.gov/ucd-icd10.html.

Selection was α = 600 and Nm = 10. Mean localization is given in distance (kb) from the real selective position.

This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system’s typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the wide applicability and usefulness of our proposed framework. We show that the proposed algorithm can work well in the presence of noisy nonstationarity data within multiple classes of time series. This framework is implemented in the open source R package oddstream. R code and data are available in the online supplementary materials.

This article introduces lookout, a new approach to detect outliers using leave-one-out kernel density estimates and extreme value theory. Outlier detection methods that use kernel density estimates generally employ a user defined parameter to determine the bandwidth. Lookout uses persistent homology to construct a bandwidth suitable for outlier detection without any user input. We demonstrate the effectiveness of lookout on an extensive data repository by comparing its performance with other outlier detection methods based on extreme value theory. Furthermore, we introduce outlier persistence, a useful concept that explores the birth and the cessation of outliers with changing bandwidth and significance levels. The R package lookout implements this algorithm. Supplementary files for this article are available online.

1) Data Introduction • The Dynamic Apparel Sales with Anomalies Dataset is based on 100,000 sales transaction data from the fashion industry, including extreme outliers, missing values, and sales_categories, reflecting the different data characteristics of real retail environments.

2) Data Utilization (1) Dynamic Apparel Sales with Anomalies Dataset has characteristics that: • This dataset consists of nine categorical variables and 10 numerical variables, including product name, brand, gender clothing, price, discount rate, inventory level, and customer behavior, making it suitable for analyzing product and customer characteristics. (2) Dynamic Apparel Sales with Anomalies Dataset can be used to: • Sales anomaly detection and quality control: Transaction data with outliers and missing values can be used to detect outliers, manage quality, refine data, and develop outlier processing techniques. • Sales Forecast and Customer Analysis Modeling: Based on a variety of product and customer characteristics, it can be used to support data-driven decision-making, such as machine learning-based sales forecasting, customer segmentation, and customized marketing strategies.

The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that outlier-augmented SV schemes do at least as well as a conventional SV model. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the period since the pandemic’s outbreak, as well as for earlier subsamples of relatively high volatility. This version has been accepted for publication in The Review of Economics and Statistics .

Weather Anomalies in the United States

Outliers from 1964-2013

By Carl V. Lewis [source]

About this dataset

Historical Weather Outliers in the United States,1964-2013:This dataset contains historical weather outliers in the United States from 1964 to 2013. The data includes thereporting station ID, name, min/max temperature, as well as degree coordinates of the recorded weather. The original weather data was collected from NOAA.

Each entry in this dataset represents a report from a weather station with high or low temperatures that were historical outliers within that month, averaged over time. This table's columns contain data that was collected from NOAA as well as data that was calculated using Enigma's assortment of weather data. The direct source of the information is identified in the description of the column.

Columns:date_str,degrees_from_mean,longitude,latitude,max_temp,min_temp,station_name,type

How to use the dataset

This dataset contains historical weather outliers in the United States from 1964 to 2013. The data includes the station ID, name, minimum and maximum temperatures, as well as degree coordinates of the recorded weather.

To use this dataset, simply download it and open it in a text editor or spreadsheet program. The data is organized by columns, with each column representing a different piece of information. Here is a brief explanation of each column:

• date_str: The date of the weather report.
• degrees_from_mean: The number of degrees that the temperature was above or below the historical mean for that month.
• longitude: The longitude of the weather station.
• latitude: The latitude of the weather station.
• max_temp: The maximum temperature reported by the weather station.
• min_temp: The minimum temperature reported by the weather station.
• station_name: The name of the weather station.
• type: The type of outlier, either high or low

Research Ideas

• Plotting the locations of outliers on a map of the US
• Identifying weather patterns associated with outliers
• Determining which areas of the US are most vulnerable to extreme weather events

Acknowledgements

This dataset was originally published by Enigma.io Analysis.

#

Data Source>

License

Unknown License - Please check the dataset description for more information.

Columns

File: weather-anomalies-1964-2013.csv | Column name | Description | |:----------------------|:----------------------------------------------------------------------------------------------------| | date_str | The date of the weather anomaly. (Date) | | degrees_from_mean | The number of degrees that the temperature was above or below the monthly mean temperature. (Float) | | longitude | The longitude of the weather station where the anomaly was recorded. (Float) | | latitude | The latitude of the weather station where the anomaly was recorded. (Float) | | max_temp | The maximum temperature recorded at the weather station on the date of the anomaly. (Float) | | min_temp | The minimum temperature recorded at the weather station on the date of the anomaly. (Float) | | station_name | The name of the weather station where the anomaly was recorded. (String) | | type | The type of anomaly, either high or low temperature. (String) |

Acknowledgements

If you use this dataset in your research, please credit Carl V. Lewis.

The quality of water level time series data strongly varies with periods of high and low quality sensor data. In this paper we are presenting the processing steps which were used to generate high quality water level data from water pressure measured at the Time Series Station (TSS) Spiekeroog. The TSS is positioned in a tidal inlet between the islands of Spiekeroog and Langeoog in the East Frisian Wadden Sea (southern North Sea). The processing steps will cover sensor drift, outlier identification, interpolation of data gaps and quality control. A central step is the removal of outliers. For this process an absolute threshold of 0.25m/10min was selected which still keeps the water level increase and decrease during extreme events as shown during the quality control process. A second important feature of data processing is the interpolation of gappy data which is accomplished with a high certainty of generating trustworthy data. Applying these methods a 10 years dataset (December 2002-December 2012) of water level information at the TSS was processed resulting in a seven year time series (2005-2011).

Additional file 15: Table S14. Data and analysis for the search for epigenetic signatures of outlier expression (Excel table).

aOutlier for extreme nucleotide-based conservation.bOutlier for extreme nucleotide-based divergence.cSee Table S11 for an exhaustive list of outlier contigs with annotation.

Additional file 2: Table S2. Gene lists with transcriptome data (TPM) for five organs of the mouse DOM populations (Excel file with ten tabs). Organ names are provided in the Tab titles. For each organ, "all_TPM"includes data for all genes above the minimal expression cutoff value, "OO"is the corresponding sub list for all genes with at least one over-outlier expression.

There has been a tremendous increase in the volume of Earth Science data over the last decade from modern satellites, in-situ sensors and different climate models. All these datasets need to be co-analyzed for finding interesting patterns or for searching for extremes or outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations. Moving these petabytes of data over the network to a single location may waste a lot of bandwidth, and can take days to finish. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the global data without moving all the data to one location. The algorithm is highly accurate (close to 99%) and requires centralizing less than 5% of the entire dataset. We demonstrate the performance of the algorithm using data obtained from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images.

This dataset captures 100,000 sales transactions in the fashion industry, featuring extreme outliers, missing values, and a multiclass classification target (Sales_Category). With 9 categorical and 10 numerical attributes, this dataset is ideal for exploratory data analysis (EDA), data visualization, and machine learning tasks. It includes details such as product names, brands, gender-specific clothing, pricing, discounts, stock levels, and customer behavior.

This subset contains documents sampled uniformly at random from allenai/olmo-mix-1124. The longest 1% of documents in the subset are then truncated to the 99th percentile in document lengths, due to some extreme outliers in document length. The train split is used for tokenizer training, and the eval split is used for evaluating tokenizer encoding efficiency.

Additional file 8: Table S8. Gene lists with transcriptome data (CPM) for Drosophila data (Excel file with twelve tabs). For Drosophila melanogaster (Dmel) there are two parts (head and body), for Drosophila simulans (Dsim) there are four populations, as indicated in the tabs. In each case, "all" includes data for all genes above the minimal expression cutoff value, "OO" is the corresponding sub list for all genes with at least one over-outlier expression

Additional file 7: Table S7. Gene lists with transcriptome data (TPM) for four organs of the human GTEx data (Excel file with eight tabs). Organ names are provided in the Tab titles. For each organ, "all_TPM"includes data for all genes above the minimal expression cutoff value, "OO"is the corresponding sub list for all genes with at least one over-outlier expression.

Additional file 12: Table S12. Data for outlier genes occurring in modules in a semi-graphic depiction (Excel file with three tabs). Table S12A: Depiction of mouse outlier modules based on shared OO in at least three individuals for gene pairs and larger groups of genes. Table S12B: Depiction of human outlier modules based on shared OO in at least three individuals for gene pairs and larger groups of genes. Tale S12C: Depiction of Drosophila outlier modules based on shared OO in at least three individuals for gene pairs and larger groups of genes.

Additional file 6: Table S6. Gene lists with transcriptome data (TPM) for brain of the mouse inbred strain C57BL/6 (Excel file with two tabs). "BL6_brain_TPM_all"includes data for all genes above the minimal expression cutoff value, "BL6_brain_OO"is the corresponding sub list for all genes with at least one over-outlier expression

Summary result of number of outliers in selected MNCH data items.

Additional file 11: Table S11. Pedigree and data for the mouse family analysis (Excel file with five tabs). Table S11A: pedigree scheme for the five families. Table S11B: data and analysis for brain. Table S11C: data and analysis for kidney. Table S11D: data and analysis for liver. Table S11E: subset of data and analysis for genes that follow Mendelian segregation ratios