Reconciling Predictions in the Regression Setting: An Application to Bus Travel Time Prediction

Mendes‐Moreira, João; Baratchi, Mitra

doi:10.1007/978-3-030-44584-3_25

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Thereby, our micro-learning approach can be considered as a divide and conquer method following this idea. Mendes-Moreira and Barachi [19] proposed a prediction model for networks by predicting sub parts of the networks and re-conciliate the aggregated predictions of the sub-parts with the path they are part of. They do this using a method they called Reconciliation For Regression (R4R) by weighting each sub-prediction using a constraint least square algorithm.…”

Section: Machine Learningmentioning

confidence: 99%

Reasoning over time into models with DataTime

et al. 2023

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Models at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of full-fledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (ie. values of properties of interest). DataTime offers a unifying interface to query the past, present and future (predicted) states of the system. This unifying interface provides i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, ii) the ability to get the last available value provided by the system's sensors, and iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This

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Section: Machine Learningmentioning

confidence: 99%

Reasoning over time into models with DataTime

et al. 2023

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“…Mendes-Moreira and Barachi [17] proposed a prediction model for networks by predicting sub parts of the networks and re-conciliate the aggregated predictions of the sub-parts with the path they are part of. They do this using a method they called Reconciliation For Regression (R4R) by weighting each sub-prediction using a constraint least square algorithm.…”

Section: Machine Learningmentioning

confidence: 99%

DataTime: a Framework to smoothly Integrate Past, Present and Future into Models

Lyan

Jézéquel

Gross-Amblard

et al. 2021

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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“…Mendes-Moreira and Barachi [16] imagined a prediction model for networks by predicting sub parts of the networks and re-conciliate the aggregated predictions of the sub-parts with the path they are part of. They do this using a method they called Reconciliation For Regression (R4R) by weighing every sub-predictions using a constraint least square algorithm.…”

Section: Bus Travel Time / Bus Speed Predictionmentioning

confidence: 99%

On the Quality of Compositional Prediction for Prospective Analytics on Graphs

Lyan¹,

Amblard²,

Jézéquel³

2021

Communications in Computer and Information Science

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Recently, micro-learning has been successfully applied to various scenarios, such as graph optimization (e.g. power grid management). In these approaches, ad-hoc models of local data are built instead of one large model on the overall data set. Micro-learning is typically useful for incremental, what-if/prospective scenarios, where one has to perform step-by-step decisions based on local properties. A common feature of these applications is that the predicted properties (such as speed of a bus line) are compositions of smaller parts (e.g. the speed on each bus inter-stations along the line). But little is known about the quality of such predictions when generalized at a larger scale.In this paper we propose a generic technique that embeds machine-learning for graph-based compositional prediction, that allows 1) the prediction of the behaviour of composite objects, based on the predictions of their sub-parts and appropriate composition rules, and 2) the production of rich prospective analytics scenarios, where new objects never observed before can be predicted based on their simpler parts. We show that the quality of such predictions compete with macro-learning ones, while enabling prospective scenarios. We assess our work on a real size, operational bus network data set.

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Reconciling Predictions in the Regression Setting: An Application to Bus Travel Time Prediction

Cited by 3 publications

References 14 publications

Reasoning over time into models with DataTime

Reasoning over time into models with DataTime

DataTime: a Framework to smoothly Integrate Past, Present and Future into Models

On the Quality of Compositional Prediction for Prospective Analytics on Graphs

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