This dataset is composed of 2000 time-series (1000 Read and 1000 Write) realized from the much larger cloud storage workload released to the research community by the Alibaba group. The original dataset can be download from here: (https://github.com/alibaba/block-traces).
This zebrafish motion dataset contains
The historical dataset of the meteorological variables was recorded by the Mexican National Water Council (Comisión Nacional del Agua, CONAGUA) ground station located at the city of Mérida, Yucatán, Mexico; the following variables were found: temperature (T), vapor pressure (P), and relative humidity (H). The range dates of the records were from January 1, 2000 to September 30, 2018, where there is a daily record of temperatures with minimum, maximum, and average units, resulting in nine readings provided for each day.
The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.
