The proliferation of IoT devices generates vast interaction data, offering insights into user behaviour. While prior work predicts what actions users perform, the timing of these actions -- critical for enabling proactive and efficient smart systems -- remains relatively underexplored. Addressing this gap, we focus on predicting the time of the next user action in smart environments. Due to the lack of public datasets with fine-grained timestamps suitable for this task and associated privacy concerns, we contribute a dataset of 11.6k sequences synthesized based on human annotations of interaction patterns, pairing actions with precise timestamps. To this end, we introduce Timing-Matters, a Transformer-Encoder based method that predicts action timing, achieving 38.30% accuracy on the synthesized dataset, outperforming the best baseline by 6%, and showing 1--6% improvements on other open datasets. Our code and dataset will be publicly released.
@article{arxiv.2411.18719,
title = {Timing Matters: Enhancing User Experience through Temporal Prediction in Smart Homes},
author = {Shrey Ganatra and Spandan Anaokar and Pushpak Bhattacharyya},
journal= {arXiv preprint arXiv:2411.18719},
year = {2025}
}