Time series analysis

The DARai dataset is a comprehensive multimodal multi-view collection designed to capture daily activities in diverse indoor environments. This dataset incorporates 20 heterogeneous modalities, including environmental sensors, biomechanical measures, and physiological signals, providing a detailed view of human interactions with their surroundings. The recorded activities cover a wide range, such as office work, household chores, personal care, and leisure activities, all set within realistic contexts.

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The training data consists of data from various faults from five individual configurations, while the testing data is blind and is from one individual configuration of the rock drill.  A final validation data set will be from two individual configurations from the rock drill and the labels are blind. 

The training data set contains data from 11 different fault classification categories, in which 10 are different failure modes and one class is from the healthy/no fault condition. 

Each file follows the naming convention of data_{sensor}{individual impact cycle number}.csv.

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Mission-integrated ⪅8nT magnetometer (MAG) data at Jupiter and Saturn, 100-minute averages. Data consist of the only available MAG measurements that spanned at least 6 months (180 Earth days), and include the Cassini–Huygens, Galileo, and Juno missions. Steven P. Joy and Joe Mafi (UCLA & NASA Planetary Data System/Planetary Plasma Interactions Node) provided concatenated and RTN-rotated Jupiter MAG 1-minute averages from Galileo and Juno, including random samples of field swelling.

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North/South-separated <10nT magnetometer data from the 1994-2008 Ulysses space mission's samplings of polar solar winds, hourly averaged, non-decimated. The set also includes data from the WIND space mission, the WSO terrestrial observatory (solar Mean Magnetic Field), and the WSO solar Polar magnetic Field, all used in the same research project. The NOAA Solar Indices data (not included) were used for verification, see paper for details.

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Distributed Denial of Service (DDoS) attacks first appeared in the mid-1990s, as attacks stopping legitimate users from accessing specific services available on the Internet. A DDoS attack attempts to exhaust the resources of the victim to crash or suspend its services. Time series modeling will help system administrators for better planning of resource allocation to defend against DDoS attacks. Different Time Series analysis techniques are applied to detect the DDoS attacks.

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This MATLAB dataset (.mat) contains the collected real measurement data from a total of 470 access points (APs) deployed in the Linnanmaa campus of the University of Oulu, Finland. The measurements include IDs, dates of data collection, number of users, received traffic data, transmitted traffic data and location names of each AP. Each observation of traffic data and number of users provide the data value at every 10-minute interval between December 18, 2018 and February 12, 2019. Please cite this as: S. P. Sone & Janne Lehtomäki & Zaheer Khan.

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