1,574 research outputs found
Self-Attentive hawkes process
Capturing the occurrence dynamics is crucial to predicting which type of events will happen next and when. A common method to do this is through Hawkes processes. To enhance their capacity, recurrent neural networks (RNNs) have been incorporated due to RNNs successes in processing sequential data such as languages. Recent evidence suggests that self-Attention is more competent than RNNs in dealing with languages. However, we are unaware of the effectiveness of self-Attention in the context of Hawkes processes. This study aims to fill the gap by designing a self-Attentive Hawkes process (SAHP). SAHP employs self-Attention to summarise the influence of history events and compute the probability of the next event. One deficit of the conventional selfattention, when applied to event sequences, is that its positional encoding only considers the order of a sequence ignoring the time intervals between events. To overcome this deficit, we modify its encoding by translating time intervals into phase shifts of sinusoidal functions. Experiments on goodness-of-fit and prediction tasks show the improved capability of SAHP. Furthermore, SAHP is more interpretable than RNN-based counterparts because the learnt attention weights reveal contributions of one event type to the happening of another type. To the best of our knowledge, this is the first work that studies the effectiveness of self-Attention in Hawkes processes
Order flow and price formation
I present an overview of some recent advancements on the empirical analysis and theoretical modeling of the process of price formation in financial markets as the result of the arrival of orders in a limit order book exchange. After discussing critically the possible modeling approaches and the observed stylized facts of order flow, I consider in detail market impact and transaction cost of trades executed incrementally over an extended period of time, by comparing model predictions and recent extensive empirical results. I also discuss how the simultaneous presence of many algorithmic trading executions affects the quality and cost of trading
Modeling Events and Interactions through Temporal Processes -- A Survey
In real-world scenario, many phenomena produce a collection of events that
occur in continuous time. Point Processes provide a natural mathematical
framework for modeling these sequences of events. In this survey, we
investigate probabilistic models for modeling event sequences through temporal
processes. We revise the notion of event modeling and provide the mathematical
foundations that characterize the literature on the topic. We define an
ontology to categorize the existing approaches in terms of three families:
simple, marked, and spatio-temporal point processes. For each family, we
systematically review the existing approaches based based on deep learning.
Finally, we analyze the scenarios where the proposed techniques can be used for
addressing prediction and modeling aspects.Comment: Image replacement
Convex Parameter Recovery for Interacting Marked Processes
We introduce a new general modeling approach for multivariate discrete event
data with categorical interacting marks, which we refer to as marked Bernoulli
processes. In the proposed model, the probability of an event of a specific
category to occur in a location may be influenced by past events at this and
other locations. We do not restrict interactions to be positive or decaying
over time as it is commonly adopted, allowing us to capture an arbitrary shape
of influence from historical events, locations, and events of different
categories. In our modeling, prior knowledge is incorporated by allowing
general convex constraints on model parameters. We develop two parameter
estimation procedures utilizing the constrained Least Squares (LS) and Maximum
Likelihood (ML) estimation, which are solved using variational inequalities
with monotone operators. We discuss different applications of our approach and
illustrate the performance of proposed recovery routines on synthetic examples
and a real-world police dataset
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process
The prevalence of location-based social networks (LBSNs) has eased the
understanding of human mobility patterns. Knowledge of human dynamics can aid
in various ways like urban planning, managing traffic congestion, personalized
recommendation etc. These dynamics are influenced by factors like social
impact, periodicity in mobility, spatial proximity, influence among users and
semantic categories etc., which makes location modelling a critical task.
However, categories which act as semantic characterization of the location,
might be missing for some check-ins and can adversely affect modelling the
mobility dynamics of users. At the same time, mobility patterns provide a cue
on the missing semantic category. In this paper, we simultaneously address the
problem of semantic annotation of locations and location adoption dynamics of
users. We propose our model HAP-SAP, a latent spatio-temporal multivariate
Hawkes process, which considers latent semantic category influences, and
temporal and spatial mobility patterns of users. The model parameters and
latent semantic categories are inferred using expectation-maximization
algorithm, which uses Gibbs sampling to obtain posterior distribution over
latent semantic categories. The inferred semantic categories can supplement our
model on predicting the next check-in events by users. Our experiments on real
datasets demonstrate the effectiveness of the proposed model for the semantic
annotation and location adoption modelling tasks.Comment: 11 page
Causal Discovery from Temporal Data: An Overview and New Perspectives
Temporal data, representing chronological observations of complex systems,
has always been a typical data structure that can be widely generated by many
domains, such as industry, medicine and finance. Analyzing this type of data is
extremely valuable for various applications. Thus, different temporal data
analysis tasks, eg, classification, clustering and prediction, have been
proposed in the past decades. Among them, causal discovery, learning the causal
relations from temporal data, is considered an interesting yet critical task
and has attracted much research attention. Existing casual discovery works can
be divided into two highly correlated categories according to whether the
temporal data is calibrated, ie, multivariate time series casual discovery, and
event sequence casual discovery. However, most previous surveys are only
focused on the time series casual discovery and ignore the second category. In
this paper, we specify the correlation between the two categories and provide a
systematical overview of existing solutions. Furthermore, we provide public
datasets, evaluation metrics and new perspectives for temporal data casual
discovery.Comment: 52 pages, 6 figure
EasyTPP: Towards Open Benchmarking Temporal Point Processes
Continuous-time event sequences play a vital role in real-world domains such
as healthcare, finance, online shopping, social networks, and so on. To model
such data, temporal point processes (TPPs) have emerged as the most natural and
competitive models, making a significant impact in both academic and
application communities. Despite the emergence of many powerful models in
recent years, there hasn't been a central benchmark for these models and future
research endeavors. This lack of standardization impedes researchers and
practitioners from comparing methods and reproducing results, potentially
slowing down progress in this field. In this paper, we present EasyTPP, the
first central repository of research assets (e.g., data, models, evaluation
programs, documentations) in the area of event sequence modeling. Our EasyTPP
makes several unique contributions to this area: a unified interface of using
existing datasets and adding new datasets; a wide range of evaluation programs
that are easy to use and extend as well as facilitate reproducible research;
implementations of popular neural TPPs, together with a rich library of modules
by composing which one could quickly build complex models. All the data and
implementation can be found at
https://github.com/ant-research/EasyTemporalPointProcess. We will actively
maintain this benchmark and welcome contributions from other researchers and
practitioners. Our benchmark will help promote reproducible research in this
field, thus accelerating research progress as well as making more significant
real-world impacts.Comment: ICLR 2024 camera read
Learning Neural Point Processes with Latent Graphs
Neural point processes (NPPs) employ neural networks to capture complicated dynamics of asynchronous event sequences. Existing NPPs feed all history events into neural networks, assuming that all event types contribute to the prediction of the target type. How- ever, this assumption can be problematic because in reality some event types do not contribute to the predictions of another type. To correct this defect, we learn to omit those types of events that do not contribute to the prediction of one target type during the formulation of NPPs. Towards this end, we simultaneously consider the tasks of (1) finding event types that contribute to predictions of the target types and (2) learning a NPP model from event se- quences. For the former, we formulate a latent graph, with event types being vertices and non-zero contributing relationships being directed edges; then we propose a probabilistic graph generator, from which we sample a latent graph. For the latter, the sampled graph can be readily used as a plug-in to modify an existing NPP model. Because these two tasks are nested, we propose to optimize the model parameters through bilevel programming, and develop an efficient solution based on truncated gradient back-propagation. Experimental results on both synthetic and real-world datasets show the improved performance against state-of-the-art baselines. This work removes disturbance of non-contributing event types with the aid of a validation procedure, similar to the practice to mitigate overfitting used when training machine learning models
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