Time Series Regression

This dataset details the energy consumption of appliances in a low-energy building over 4.5 months. Data was collected at 10-minute intervals. For more information about the house, data collection, R scripts and figures, please refer to the paper and the following GitHub repository: https://github.com/LuisM78/Appliances-energy-prediction-data

The HEterogeneous Materials and Elastic Waves with Source variability in 3D (HEMEWS-3D) database comprises 30,000 simulations of elastic wave propagation in 3D geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. Elastic waves originate from a randomly located pointwise source parametrized by a random moment tensor. For each simulation, ground motion is synthesized at the surface by a grid of virtual sensors. The high frequency of waveforms ($f_{max}$ = 5 Hz) allows for extensive analyses of surface ground motion. Existing and foreseen applications range from statistical analyses of the ground motion variability and machine learning methods on geological models to deep-learning-based predictions of ground motion that depend on 3D heterogeneous geologies and source properties.

PPG-DaLiA is a publicly available dataset for PPG-based heart rate estimation. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects while performing a wide range of activities under close to real-life conditions. The included ECG data provides heart rate ground truth. The included PPG- and 3D-accelerometer data can be used for heart rate estimation, while compensating for motion artefacts.

COVID19 Data from the World Health Organization

a dataset of multi-modal signals from wearable devices at four sites on the body. Each device continuously recorded synchronized signals from a 3-channel reflective photoplethysmogram (red, green, infrared PPG), 3-axis inertial sensor (accelerometer), temperature, and barometric altitude sensor. For reference, the sternum device continuously recorded a Lead-I electrocardiogram (ECG) from body-mounted gel electrodes to provide ground-truth heart rate (HR) estimates.

Predictions of energy consumption are crucial for energy retailers to minimize deviations from energy acquired in the day-ahead market and the actual consumption of their customers. The increasing spread of smartmeters means that retailers have access to hourly consumption values of all their contracted customers in realtime. Using machine learning algorithms, these hourly values can be used to calculate predictions for the future energy consumption of the customers. The present data set allows the training and validation of AI-based prediction models.

Enhancing Financial Market Predictions: Causality-Driven Feature Selection This paper introduces FinSen dataset that revolutionizes financial market analysis by integrating economic and financial news articles from 197 countries with stock market data. The dataset’s extensive coverage spans 15 years from 2007 to 2023 with temporal information, offering a rich, global perspective 160,000 records on financial market news. Our study leverages causally validated sentiment scores and LSTM models to enhance market forecast accuracy and reliability. # Our FinSen Dataset arXiv Pytorch 1.5 License: MIT This repository contains the dataset for Enhancing Financial Market Predictions: Causality-Driven Feature Selection, which has been accepted in ADMA 2024. If the dataset or the paper has been useful in your research, please add a citation to our work: @article{liang2024enhancing, title={Enhancing Financial Market Predictions: Causality-Driven Feature Selection}, author={Liang, Wenhao and Li, Zhengyang and Chen, Weitong}, journal={arXiv e-prints}, pages={arXiv--2408}, year={2024} } ### Datasets [FinSen] can be downloaded manually from the repository as csv file. Sentiment and its score are generated by FinBert model from the Hugging Face Transformers library under the identifier "ProsusAI/finbert". (Araci, Dogu. "Finbert: Financial sentiment analysis with pre-trained language models." arXiv preprint arXiv:1908.10063 (2019).) We only provide US for research purpose usage, please contact w.liang@adelaide.edu.au for other countries (total 197 included) if necessary. We also provide other NLP datasets for text classification tasks here, please cite them correspondingly once you used them in your research if any. 1. 20Newsgroups. Joachims, T., et al.: A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In: ICML. vol. 97, pp. 143–151. Citeseer (1997) 2. AG News. Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015) 3. Financial PhraseBank. Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology 65(4), 782–796 (2014) ### Dataloader for FinSen We provide the preprocessing file finsen.py for our FinSen dataset under dataloaders directory for more convienient usage. ### Models - Text Classification 1. DAN-3. 2. Gobal Pooling CNN. ### Models - Regression Prediction 1. LSTM ### Using Sentiment Score from FinSen Predict Result on S&P500 ### Dependencies The code is based on PyTorch under code frame of https://github.com/torrvision/focal_calibration, please cite their work if you found it is useful. :smiley: ☺ Happy Research !

The Mauna Loa Seeing Study was performed by the EOL/Integrated Surface Flux System team, capturing surface meteorology and flux products at the Mauna Loa Observatory in Hawaii. The MLO-Cn2 dataset enables researchers to evaluate physics-inspired models for optical turbulence ($C_n^2$), as well as develop new models from sonic anemometer data. ## Summary Sonic anemometers were deployed during Summer 2006 at the Mauna Loa Observatory, Hawaii to quantify the atmospheric seeing quality, to help NCAR/HAO plan current and future telescope observations. This study was intended to replicate measurements made previously for planning the Advanced Technology Solar Telescope (ATST). Deployment and operations for this study were done by HAO staff, and ISFF provided equipment and some data management support. ## Details Identifiers * Local archive identifier: 160.007 * doi:10.26023/CQR2-TQJ9-AH10 https://doi.org/10.26023/CQR2-TQJ9-AH10 Related projects * MLO_CN2: Mauna Loa Seeing Study (Cn-squared study) https://data.eol.ucar.edu/project/MLO_CN2 Additional information * Frequency: 5 minute * Language: English Categories * Surface Platforms * Flux Tower * NCAR/EOL Integrated Surface Flux System - ISFS Instruments * Eddy Correlation Devices * Sonic Anemometer * Surface Flux * Surface Meteorology Related links * homepage: MLO_CN2 project main web page http://www.eol.ucar.edu/isf/projects/MLO_CN2/ * info: Integrated Surface Flux System https://www.eol.ucar.edu/observing_facilities/isfs * homepage: MLO_CN2 Project Homepage https://www.eol.ucar.edu/field_projects/mlocn2 Temporal coverage * Begin datetime: 2006-06-09 00:00:00 * End datetime: 2006-08-08 23:59:59 Spatial coverage * Maximum (North) Latitude: 19.53 * Minimum (South) Latitude: 19.53 * Minimum (West) Longitude: -155.57 * Maximum (East) Longitude: -155.57 Primary contact information * pointOfContact: EOL Data Support datahelp@eol.ucar.edu https://data.eol.ucar.edu/contact/show/1 Additional contact information * author: NSF NCAR/EOL ISFS Team https://data.eol.ucar.edu/contact/show/3140 Alternate metadata formats * DataCite: https://data.eol.ucar.edu/dataset/160.007?format=datacite * ISO/TC 211: https://data.eol.ucar.edu/dataset/160.007?format=isotc211

This dataset is developed to estimate bearing loads under various operating conditions (rotational speed, axial and radial loads) using data from temperature and vibration sensors. These sensor modalities provide complementary information: vibration signals indicate the magnitude of the load, while temperature measurements reveal the spatial distribution of the load within the bearing. The dataset emulates a real-world deployment scenario of a virtual sensor, mirroring scenarios where a physical sensor's operational life is limited, such as when a sensor roller collecting field data experiences premature battery depletion. It contains 55 unique operating conditions, defined by axial load (Fx), radial load (Fy), and rotational speed.

The USNA long-term scintillation study is a continuing effort to characterize and measure optical turbulence in the near-maritime boundary layer. ## Summary The field campaign utilized a ScinTec BLS450 scintillometer to characterize the strength of optical turbulence over a link approximately 3 [m] above the river’s surface between January 1\textsuperscript{st}, 2020, and July 26\textsuperscript{th}, 2023. This field campaign included a range of meteorological and oceanographic measurements from nearby NOAA, National Data Buoy Center (NDBC), and Academy weather stations. The long-term dataset covers the period between January 1\textsuperscript{st}, 2020 and July 14\textsuperscript{th}, 2022. This data set includes local measurements made with a Davis Vantage Pro2 weather station, as well as water conditions from the NDBC Thomas Point data buoy. ## Details Additional Information * Frequency: 1 minute * Language: English Categories * Surface Platforms * National Data Buoy Center station TPLM2 Instruments * ScinTec BLS450 * Davis Vantage Pro2 Temporal coverage * Begin datetime: 2020-01-01 00:00:00 * End datetime: 2022-07-14 00:00:00 Spatial coverage * Maximum (North) Latitude: 38.98 * Minimum (South) Latitude: 38.98 * Minimum (West) Longitude: -76.48 * Maximum (East) Longitude: -76.48 Primary contact information * author: Chris Jellen cjellen@microsoft.com

The USNA long-term scintillation study is a continuing effort to characterize and measure optical turbulence in the near-maritime boundary layer. ## Summary The field campaign utilized a ScinTec BLS450 scintillometer to characterize the strength of optical turbulence over a link approximately 3 [m] above the river’s surface between January 1\textsuperscript{st}, 2020, and July 26\textsuperscript{th}, 2023. This field campaign included a range of meteorological and oceanographic measurements from nearby NOAA, National Data Buoy Center (NDBC), and Academy weather stations. The short-duration dataset includes measured $C_n^2$ along with atmospheric and oceanographic parameters from a co-located NOAA COOPS observation station between June 1\textsuperscript{st}, 2021, and September 1\textsuperscript{st}, 2021. ## Details Additional information * Frequency: 6 minute * Language: English Categories * Surface Platforms * NOAA Coastal Observation Station 8575512 Instruments * ScinTec BLS450 * Davis Vantage Pro2 Temporal coverage * Begin datetime: 2021-06-01 00:00:00 * End datetime: 2021-09-01 00:00:00 Spatial coverage * Maximum (North) Latitude: 38.98 * Minimum (South) Latitude: 38.98 * Minimum (West) Longitude: -76.48 * Maximum (East) Longitude: -76.48 Primary contact information * author: Chris Jellen cjellen@microsoft.com