Tracking uncertainty in a spatially explicit susceptible-infected epidemic model

In this paper we conceive an interval-valued continuous cellular automaton for describing the spatio-temporal dynamics of an epidemic, in which the magnitude of the initial outbreak and/or the epidemic properties are only imprecisely known. In contrast to well-established approaches that rely on probability distributions for keeping track of the uncertainty in spatio-temporal models, we resort to an interval representation of uncertainty. Such an approach lowers the amount of computing power that is needed to run model simulations, and reduces the need for data that are indispensable for constructing the probability distributions upon which other paradigms are based

Temporal information gaps and market efficiency: A dynamic behavioral analysis

This study seeks to explore, how market efficiency changes, if ordinary traders receive fundamental news more or less often. We show that longer temporal information gaps lead to fewer but larger shocks and a reduction of the average noise level on the dynamics. The consequences of these effects for market efficiency are ambiguous. Longer temporal information gaps can deteriorate or improve market efficiency. The concrete result depends on the stability of the market together with the interval in which the length of the gap is incremented. --Temporal information gaps,market efficiency,disclosure policy,agent-based financial market models,technical and fundamental analysis

Qualitative models for planning: A gentle introduction

Qualitative modeling is the study of how the physical world behaves. These physical models accept partial descriptions of the world and output the possible changes. Current systems assume that the model is static and that physical entities do not effect change into the world. An approach to planning in physical domains and a working implementation which integrates qualitative models with a temporal interval-based planner are described. The planner constructs plans involving physical qualities and their behavioral descriptions

Graph based management of temporal data

In recent decades, there has been a significant increase in the use of smart devices and sensors that led to high-volume temporal data generation. Temporal modeling and querying of this huge data have been essential for effective querying and retrieval. However, custom temporal models have the problem of generalizability, whereas the extended temporal models require users to adapt to new querying languages. In this thesis, we propose a method to improve the modeling and retrieval of temporal data using an existing graph database system (i.e., Neo4j) without extending with additional operators. Our work focuses on temporal data represented as intervals (event with a start and end time). We propose a novel way of storing temporal interval as cartesian points where the start time and the end time are stored as the x and y axis of the cartesian coordinate. We present how queries based on Allen’s interval relationships can be represented using our model on a cartesian coordinate system by visualizing these queries. Temporal queries based on Allen’s temporal intervals are then used to validate our model and compare with the traditional way of storing temporal intervals (i.e., as attributes of nodes). Our experimental results on a soccer graph database with around 4000 games show that the spatial representation of temporal interval can provide significant performance (up to 3.5 times speedup) gains compared to a traditional model

Interval Temporal Random Forests with an Application to COVID-19 Diagnosis

Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one

Temporal Reasoning without Transitive Tables

rapport interneRepresenting and reasoning about qualitative temporal information is an essential part of many artificial intelligence tasks. Lots of models have been proposed in the litterature for representing such temporal information. All derive from a point-based or an interval-based framework. One fundamental reasoning task that arises in applications of these frameworks is given by the following scheme: given possibly indefinite and incomplete knowledge of the binary relationships between some temporal objects, find the consistent scenarii between all these objects. All these models require transitive tables --- or similarly inference rules--- for solving such tasks. We have defined an alternative model, S-languages - to represent qualitative temporal information, based on the only two relations of \emph{precedence} and \emph{simultaneity}. In this paper, we show how this model enables to avoid transitive tables or inference rules to handle this kind of problem