Research and Publications
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: A Continual Learning Approach Leveraging Human Mobility
Arian Prabowo, Kaixuan Chen, Hao Xue, Subbu Sethuvenkatraman, and Flora D. Salim. 2023. Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: A Continual Learning Approach Leveraging Human Mobility. In The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’23), November 13-14, 2023, Istanbul, Turkiye. https://doi.org/10.1145/3600100.3623726
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption becomes problematic when faced with Out-of-Distribution periods, such as the COVID-19 lockdowns, where the data distribution significantly deviates from what the model has seen during training.
This paper employs a two-fold strategy: utilizing continual learning techniques to update models with new data and harnessing human mobility data collected from privacy-preserving pedestrian counters located outside buildings. In contrast to online learning, which suffers from 'catastrophic forgetting' as newly acquired knowledge often erases prior information, continual learning offers a holistic approach by preserving past insights while integrating new data.
This research applies FSNet, a powerful continual learning algorithm, to real-world data from 13 building complexes in Melbourne, Australia, a city which had the second longest total lockdown duration globally during the pandemic. Results underscore the crucial role of continual learning in accurate energy forecasting, particularly during Out-of-Distribution periods. Secondary data such as mobility and temperature provided ancillary support to the primary forecasting model. More importantly, while traditional methods struggled to adapt during lockdowns, models featuring at least online learning demonstrated resilience, with lockdown periods posing fewer challenges once armed with adaptive learning techniques.
This study contributes valuable methodologies and insights to the ongoing effort to improve energy load forecasting during future Out-of-Distribution periods.
The complete results, slides, and talk can be found in the official GitHub.
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. This importance is magnified during anomalous periods, such as the COVID-19 pandemic, which have disrupted occupancy patterns and made accurate forecasting more challenging.
Forecasting energy usage during anomalous periods is difficult due to changes in occupancy patterns and energy usage behavior. One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home. This has created a need for new forecasting methods that can adapt to changing occupancy patterns.
Online learning has emerged as a promising solution to this challenge, as it enables building managers to adapt to changes in occupancy patterns and adjust energy usage accordingly. With online learning, models can be updated incrementally with each new data point, allowing them to learn and adapt in real-time.
Another solution is to use human mobility data as a proxy for occupancy, leveraging the prevalence of mobile devices to track movement patterns and infer occupancy levels. Human mobility data can be useful in this context as it provides a way to monitor occupancy patterns without relying on traditional sensors or manual data collection methods.
We have conducted extensive experiments using data from six buildings to test the efficacy of these approaches. However, deploying these methods in the real world presents several challenges.
Talk, slides, and poster can be found in the official GitHub: https://github.com/aprbw/OoD_Electricity_Forecasting_during_COVID-19
Prabowo, A., Chen, K., Xue, H., Sethuvenkatraman, S., Salim, F.D. (2023). Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_1
Traffic Forecasting on New Roads Using
Spatial Contrastive Pre-Training (SCPT)
Prabowo, A., Xue, H., Shao, W., Koniusz, P., Salim, F.D.. Traffic forecasting on new roads using spatial contrastive pre-training (SCPT). Data Min Knowl Disc (2023). https://doi.org/10.1007/s10618-023-00982-0
Presented in ECML PKDD 2023
Code, poster, slides, and talk, can be found in the official GitHub repo: https://github.com/cruiseresearchgroup/forecasting-on-new-roads
New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future.
Because Every Sensor Is Unique, so Is Every Pair:
Handling Dynamicity in Traffic Forecasting
Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the dynamics at each sensor are unique. For example, the afternoon peaks at sensors near schools are more likely to occur earlier than those near residential areas.
In this paper, we first analyze real-world traffic data to show that each sensor has a unique dynamic. Further analysis also shows that each pair of sensors also has a unique dynamic. Then, we explore how node embedding learns the unique dynamics at every sensor location. Next, we propose a novel module called Spatial Graph Transformers (SGT) where we use node embedding to leverage the self-attention mechanism to ensure that the information flow between two sensors is adaptive with respect to the unique dynamic of each pair. Finally, we present Graph Self-attention WaveNet (G-SWaN) to address the complex, non-linear spatiotemporal traffic dynamics.
Through empirical experiments on four real-world, open datasets, we show that the proposed method achieves superior performance on both traffic speed and flow forecasting. Code is available at: https://github.com/aprbw/G-SWaN.
Arian Prabowo, Wei Shao, Hao Xue, Piotr Koniusz, and Flora D. Salim. 2023. Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting. In International Conference on Internet-of-Things Design and Implementation (IoTDI ’23), May 9–12, 2023, San Antonio, TX, USA. ACM, New York, NY, USA, 20 pages. https://doi.org/10.1145/3576842.3582362
PhD thesis: Spatiotemporal Deep Learning
A. PRABOWO, “Spatiotemporal deep learning,” RMIT University, 2022. [Online]. Available: https://researchrepository.rmit.edu.au/esploro/outputs/doctoral/Spatiotemporal-deep-learning/9922229712001341#file-0
As spatiotemporal sensors become cheaper, spatiotemporal data become more widespread. At the same time, deep learning continues to be the de facto method to extract good representation in multiple applications domains. However, there are several challenges specific to spatiotemporal data. First, much spatiotemporal data are noisy, and general domain-agnostic denoising techniques do not always achieve the best results. Second, most approaches assume that data from nearby locations have similar dynamics. This assumptions is called homophily and it is not always true. However, this assumption is heavily relied upon in deep representation learning in other domains. Next, spatiotemporal data have complex local interactions. However, most approaches borrow deep learning architectures that are suitable in other domains but have insufficient expressive power for spatiotemporal data, resulting in the degradation of the latent representation. Finally, most approaches are trained end-to-end, requiring expensive retraining whenever there are changes in the road network. This research addresses the above challenges.
Message Passing Neural Networks for Traffic Forecasting
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Traffic forecasting is interesting because it is complex as the future speed of a road is dependent on a number of different factors. Therefore, to properly forecast traffic, we need a model that is capable of capturing all these different factors. A factor that is missing from the existing works is the node interactions factor. Existing works fail to capture the inter-node interactions because none are using the message-passing flavor of GNN, which is the one best suited to capture the node interactions This paper presents a plausible scenario in road traffic where node interactions are important and argued that the most appropriate GNN flavor to capture node interactions is message-passing. Results from real-world data show the superiority of the message-passing flavor for traffic forecasting. An additional experiment using synthetic data shows that the message-passing flavor can capture inter-node interaction better than other flavors.
Work in progress:
Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, and Flora D. Salim. Message Passing Neural Networks for Traffic Forecasting, 2023. arXiv:2305.05740. https://doi.org/10.48550/arXiv.2305.05740.
Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.
Wei Shao, Arian Prabowo, Sichen Zhao, Piotr Koniusz, Flora D. Salim. 2021. Predicting flight delay with spatio-temporal trajectory convolutional network and airport situational awareness map, Neurocomputing, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2021.04.136.
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D. Salim. 2020. Generative Adversarial Networks for Spatio-temporal Data: A Survey. ACM Transactions on Intelligent Systems and Technology (TIST), 13(2), pp.1-25. DOI: 10.1145/3474838
Generative Adversarial Networks (GANs) have shown remarkable success in the computer vision area for producing realistic-looking images. Recently, GAN-based techniques are shown to be promising for spatiotemporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision been presented, nobody has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we conduct a comprehensive review of the recent developments of GANs in spatio-temporal data. we summarise the popular GAN architectures in spatio-temporal data and common practices for evaluating the performance of spatio-temporal applications with GANs. In the end, we point out the future directions with the hope of benefiting researchers interested in this area.
Flight Delay Prediction using Airport Situational Awareness Map
The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and flight information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.
Wei Shao, Arian Prabowo, Sichen Zhao, Siyu Tan, Piotr Koniusz, Jefrey Chan, Xinhong Hei, Bradley Feest, and Flora D. Salim. 2019. Flight Delay Prediction using Airport Situational Awareness Map. In 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’19), November 5ś8, 2019, Chicago, IL, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3347146.3359079
COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.
Arian Prabowo, Piotr Koniusz, Wei Shao, and Flora D. Salim. 2019. COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference. In The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’19), November 13–14, 2019, New York, NY, USA. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3360322.3360853
For research during my undergraduate, please check the Bachelor of Science section in the Education page.
BibTex
@InProceedings{prabowo2023energyECMLPKDD, author="Prabowo, Arian and Chen, Kaixuan and Xue, Hao and Sethuvenkatraman, Subbu and Salim, Flora D.", editor="De Francisci Morales, Gianmarco and Perlich, Claudia and Ruchansky, Natali and Kourtellis, Nicolas and Baralis, Elena and Bonchi, Francesco", title="Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting", booktitle="Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="3--19", isbn="978-3-031-43430-3"}
@phdthesis{PRABOWOArian2022StDL,title = {Spatiotemporal deep learning},address = {},author = {PRABOWO, Arian},keywords = {Spatiotemporal;Deep Learning;Timeseries Forecasting;Traffic and Transportation;Self-Supervised Learning},school = {RMIT University},additional_subjects = {Stream and sensor data;Deep learning;Spatial data and applications},year = {2022},}
@inproceedings{prabowo2023GSWaN, author = {Prabowo, Arian and Shao, Wei and Xue, Hao and Koniusz, Piotr and Salim, Flora D.}, title = {Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting}, year = {2023}, isbn = {9798400700378}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3576842.3582362}, doi = {10.1145/3576842.3582362}, pages = {93–104}, numpages = {12}, keywords = {sensor networks, cyber-physical systems, intelligent transport systems, spatio-temporal}, location = {San Antonio, TX, USA}, series = {IoTDI '23}}
@article{prabowo2023SCPT, title={Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training}, author={Prabowo, Arian and Xue, Hao and Shao, Wei and Koniusz, Piotr, and Salim, Flora D.}, journal={Data Mining and Knowledge Discovery}, year={2023}, publisher={Springer}}
@misc{prabowo2023MPNN4TrafficForecasting, title={Message Passing Neural Networks for Traffic Forecasting}, author={Arian Prabowo and Hao Xue and Wei Shao and Piotr Koniusz and Flora D. Salim}, year={2023}, eprint={2305.05740}, archivePrefix={arXiv}, primaryClass={cs.LG}}
@article{shao2022predicting, title={Predicting flight delay with spatio-temporal trajectory convolutional network and airport situational awareness map}, author={Shao, Wei and Prabowo, Arian and Zhao, Sichen and Koniusz, Piotr and Salim, Flora D}, journal={Neurocomputing}, volume={472}, pages={280--293}, year={2022}, publisher={Elsevier}}
@inproceedings{prabowo2019coltrane, title={Coltrane: Convolutional trajectory network for deep map inference}, author={Prabowo, Arian and Koniusz, Piotr and Shao, Wei and Salim, Flora D}, booktitle={Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, pages={21--30}, year={2019}}
@inproceedings{shao2019flight, title={Flight delay prediction using airport situational awareness map}, author={Shao, Wei and Prabowo, Arian and Zhao, Sichen and Tan, Siyu and Koniusz, Piotr and Chan, Jeffrey and Hei, Xinhong and Feest, Bradley and Salim, Flora D}, booktitle={Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, pages={432--435}, year={2019}}
@article{gao2022generative, title={Generative adversarial networks for spatio-temporal data: A survey}, author={Gao, Nan and Xue, Hao and Shao, Wei and Zhao, Sichen and Qin, Kyle Kai and Prabowo, Arian and Rahaman, Mohammad Saiedur and Salim, Flora D}, journal={ACM Transactions on Intelligent Systems and Technology (TIST)}, volume={13}, number={2}, pages={1--25}, year={2022}, publisher={ACM New York, NY}}