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What Are Outliers?01:12

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
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Towards Outlier Sensor Detection in Ambient Intelligent Platforms-A Low-Complexity Statistical Approach.

Diego Martín1, Damaris Fuentes-Lorenzo1, Borja Bordel2

  • 1ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain.

Sensors (Basel, Switzerland)
|August 6, 2020
PubMed
Summary

This study identifies the best outlier detection method for sensor networks, recommending the Auto-Regressive Integrated Moving Average (ARIMA) model for its efficiency. It also provides valuable datasets for future research on reliable data management.

Keywords:
Ambient Intelligence platformabnormal databinary classifieroutlier detectionprediction modelsensor

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Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Sensor networks in smart cities generate vast, heterogeneous data streams.
  • Detecting outliers is critical for maintaining secure and reliable databases in these environments.
  • Existing outlier detection methods often require significant computational and storage resources, unsuitable for sensor nodes.

Purpose of the Study:

  • To analyze and compare three statistical prediction models for outlier detection in sensor networks.
  • To identify a low-complexity model that minimizes memory consumption and computational time.
  • To provide validated datasets for future research in real-world outlier detection.

Main Methods:

  • Evaluation of three binary classifiers based on Auto-Regressive Integrated Moving Average (ARIMA), Generalized Additive Model (GAM), and LOcal RegrESSion (LOESS) models.
  • Focus on models with low memory footprint and computational time suitable for resource-constrained sensor nodes.
  • Development of real-world classified datasets to serve as ground truth.

Main Results:

  • The Auto-Regressive Integrated Moving Average (ARIMA) model demonstrated superior performance as a classifier for outlier detection.
  • The study identified optimal settings for the ARIMA model for effective outlier identification.
  • Two real-world datasets, classified for outlier detection, were generated.

Conclusions:

  • The ARIMA model is the recommended approach for efficient outlier detection in sensor network environments.
  • The developed datasets offer valuable resources for advancing research in real-world outlier detection and data reliability.
  • The findings contribute to the development of more robust and efficient data management strategies for smart city applications.