This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using SensingKit which collects information from Core Motion framework on iOS devices. All data is collected in 50Hz sample rate. A total of 24 participants in a range of gender, age, weight, and height performed 6 activities in 15 trials in the same environment and conditions: downstairs, upstairs, walking, jogging, sitting, and standing.
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The original dataset from Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting contains traffic readings collected from 207 loop detectors on highways in Los Angeles County, aggregated in 5 minutes intervals over four months between March 2012 and June 2012.
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The original dataset from Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting contains 6 months of traffic readings from 01/01/2017 to 05/31/2017 collected every 5 minutes by 325 traffic sensors in San Francisco Bay Area. The measurements are provided by California Transportation Agencies (CalTrans) Performance Measurement System (PeMS).
Unified Time Series Dataset (UTSD) includes 7 domains with up to 1 billion time points with hierarchical capacities to facilitate research of large models in the field of time series. It is meticulously assembled from a blend of publicly accessible online data repositories and empirical data derived from real-world machine operations. We analyze each dataset within the collection, examining the time series through the lenses of stationarity and forecastability to allows us to characterize the level of complexity inherent to each dataset.
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