Publications

@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}

}