forecasting

This dataset is a unified compilation of the Electricity Transformer Temperature (ETT) datasets: ETTh1, ETTh2, ETTm1, and ETTm2. It includes both hourly and minute-level temperature and load data collected from power transformers, which are vital for developing and benchmarking time-series forecasting models. The dataset contains features such as high and medium voltage transformer temperatures (HUFL, HULL, MUFL, MULL) and the operational temperature (OT), which serves as the primary prediction target.

This file contains the source codes of the proposed end-nodes for a Wireless Sensor Network (WSN) for hydrometeorological monitoring in the article entitled "Hydrometeorological Monitoring using Wireless Sensor Networks". These codes were developed to perform LoRaWAN communication range tests and to test two distinct sensor nodes with different functionalities: a meteorological sensor node and a hydrological sensor node.

This data repository comprises three distinct datasets tailored for different predictive modeling tasks. The first dataset is a synthetic dataset designed to simulate multivariate time series patterns, incorporating both linear and non-linear dependencies among input and target features. The second dataset, the Beijing Air Quality PM2.5 dataset, consists of PM2.5 measurements alongside meteorological data like temperature, humidity, and wind speed, with the objective of predicting PM2.5 concentrations.

The unfolding of the COVID-19 outbreak was an unprecedented and unanticipated opportunity to understand how a sudden global shock modulates people’s online searches when seeking information about their emotional well-being. Furthermore, it also illustrated how public health surveillance systems were essential for tracking diseases’ spatial and temporal dynamics and shaping rapid public policy changes.