Publications
2024
1.
Nejad, Amin Shoari; Alaiz-Rodríguez, Rocío; McCarthy, Gerard D; Kelleher, Brian; Grey, Anthony; Parnell, Andrew
SERT: A transformer based model for multivariate temporal sensor data with missing values for environmental monitoring Artículo de revista
En: Computers & Geosciences, vol. 188, pp. 105601, 2024, (Publisher: Pergamon).
Resumen | Enlaces | BibTeX | Etiquetas: Environmental Monitoring, Missing Data, Spatio-Temporal Forecasting, Transformer Models
@article{nejad_sert_2024,
title = {SERT: A transformer based model for multivariate temporal sensor data with missing values for environmental monitoring},
author = {Amin Shoari Nejad and Rocío Alaiz-Rodríguez and Gerard D McCarthy and Brian Kelleher and Anthony Grey and Andrew Parnell},
url = {https://www.sciencedirect.com/science/article/pii/S0098300424000840},
year = {2024},
date = {2024-01-01},
journal = {Computers & Geosciences},
volume = {188},
pages = {105601},
abstract = {This research focuses on environmental monitoring and introduces two models for spatio-temporal forecasting that can handle missing values in sensor data without the need for imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), utilizes a transformer-based approach. The second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and more interpretable model. Extensive experiments show that these models perform competitively or better than existing state-of-the-art models for multivariate spatio-temporal forecasting.},
note = {Publisher: Pergamon},
keywords = {Environmental Monitoring, Missing Data, Spatio-Temporal Forecasting, Transformer Models},
pubstate = {published},
tppubtype = {article}
}
This research focuses on environmental monitoring and introduces two models for spatio-temporal forecasting that can handle missing values in sensor data without the need for imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), utilizes a transformer-based approach. The second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and more interpretable model. Extensive experiments show that these models perform competitively or better than existing state-of-the-art models for multivariate spatio-temporal forecasting.
2023
2.
Nejad, Amin Shoari; Alaiz-Rodríguez, Rocío; McCarthy, Gerard D; Kelleher, Brian; Grey, Anthony; Parnell, Andrew
SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring Artículo de revista
En: arXiv preprint arXiv:2306.03042, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: Artificial Neural Networks, Missing Data Handling, Spatio-Temporal Forecasting, Trasformer Models
@article{nejad_sert_2023,
title = {SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring},
author = {Amin Shoari Nejad and Rocío Alaiz-Rodríguez and Gerard D McCarthy and Brian Kelleher and Anthony Grey and Andrew Parnell},
url = {https://ui.adsabs.harvard.edu/abs/2024CG....18805601N/abstract},
year = {2023},
date = {2023-01-01},
journal = {arXiv preprint arXiv:2306.03042},
abstract = {This work introduces two models for spatio-temporal forecasting that effectively handle missing values in multivariate time series data without imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), is transformer-based, while the second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and interpretable approach. Extensive experiments on two datasets show that both models outperform or match state-of-the-art performance in handling missing data for multivariate spatio-temporal forecasting.},
keywords = {Artificial Neural Networks, Missing Data Handling, Spatio-Temporal Forecasting, Trasformer Models},
pubstate = {published},
tppubtype = {article}
}
This work introduces two models for spatio-temporal forecasting that effectively handle missing values in multivariate time series data without imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), is transformer-based, while the second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and interpretable approach. Extensive experiments on two datasets show that both models outperform or match state-of-the-art performance in handling missing data for multivariate spatio-temporal forecasting.
3.
Nejad, Amin Shoari; Alaiz-Rodríguez, Rocío; McCarthy, Gerard D; Kelleher, Brian; Grey, Anthony; Parnell, Andrew
SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring Artículo de revista
En: arXiv e-prints, pp. arXiv–2306, 2023.
Resumen | Enlaces | BibTeX | Etiquetas: Artificial Neural Networks, Missing Data Handling, Spatio-Temporal Forecasting, Trasformer Models
@article{shoari_nejad_sert_2023,
title = {SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring},
author = {Amin Shoari Nejad and Rocío Alaiz-Rodríguez and Gerard D McCarthy and Brian Kelleher and Anthony Grey and Andrew Parnell},
url = {https://arxiv.org/abs/2306.03042},
year = {2023},
date = {2023-01-01},
journal = {arXiv e-prints},
pages = {arXiv–2306},
abstract = {This research focuses on environmental monitoring and introduces two models for spatio-temporal forecasting that can handle missing values in sensor data without the need for imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), utilizes a transformer-based approach. The second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and more interpretable model. Extensive experiments show that these models perform competitively or better than existing state-of-the-art models for multivariate spatio-temporal forecasting.},
keywords = {Artificial Neural Networks, Missing Data Handling, Spatio-Temporal Forecasting, Trasformer Models},
pubstate = {published},
tppubtype = {article}
}
This research focuses on environmental monitoring and introduces two models for spatio-temporal forecasting that can handle missing values in sensor data without the need for imputation. The first model, SERT (Spatio-temporal Encoder Representations from Transformers), utilizes a transformer-based approach. The second, SST-ANN (Sparse Spatio-Temporal Artificial Neural Network), is a simpler and more interpretable model. Extensive experiments show that these models perform competitively or better than existing state-of-the-art models for multivariate spatio-temporal forecasting.