tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data

• URL: http://arxiv.org/abs/2407.01356v1
• Date: Mon, 1 Jul 2024 15:10:55 GMT
• Title: tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
• Authors: Christos Chatzis, Carla Schenker, Max Pfeffer, Evrim Acar,
• Abstract summary: We introduce t(emporal)PARAFAC2 which utilizes temporal smoothness regularization on the evolving factors. Our numerical experiments on both simulated and real datasets demonstrate the effectiveness of the temporal smoothness regularization.
• Score: 0.7285444492473742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tensor factorizations have been widely used for the task of uncovering patterns in various domains. Often, the input is time-evolving, shifting the goal to tracking the evolution of underlying patterns instead. To adapt to this more complex setting, existing methods incorporate temporal regularization but they either have overly constrained structural requirements or lack uniqueness which is crucial for interpretation. In this paper, in order to capture the underlying evolving patterns, we introduce t(emporal)PARAFAC2 which utilizes temporal smoothness regularization on the evolving factors. We propose an algorithmic framework that employs Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM) to fit the model. Furthermore, we extend the algorithmic framework to the case of partially observed data. Our numerical experiments on both simulated and real datasets demonstrate the effectiveness of the temporal smoothness regularization, in particular, in the case of data with missing entries. We also provide an extensive comparison of different approaches for handling missing data within the proposed framework.

Related papers

• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms [0.393259574660092]
  RHiOTS is designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive.
  arXiv  Detail & Related papers  (2024-08-06T18:52:15Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• Embedded feature selection in LSTM networks with multi-objective evolutionary ensemble learning for time series forecasting [49.1574468325115]
  We present a novel feature selection method embedded in Long Short-Term Memory networks. Our approach optimize the weights and biases of the LSTM in a partitioned manner. Experimental evaluations on air quality time series data from Italy and southeast Spain demonstrate that our method substantially improves the ability generalization of conventional LSTMs.
  arXiv  Detail & Related papers  (2023-12-29T08:42:10Z)
• A Time-aware tensor decomposition for tracking evolving patterns [0.7958824725263767]
  Time-evolving data sets can often be arranged as a higher-order tensor with one of the modes being the time mode. While tensor factorizations have been successfully used to capture the underlying patterns in such higher-order data sets, the temporal aspect is often ignored. We propose temporal PARAFAC2: a PARAFAC2-based tensor factorization method with temporal regularization to extract gradually evolving patterns from temporal data.
  arXiv  Detail & Related papers  (2023-08-14T13:13:50Z)
• OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning [67.07363529640784]
  We propose OpenSTL to categorize prevalent approaches into recurrent-based and recurrent-free models. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and forecasting weather. We find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models.
  arXiv  Detail & Related papers  (2023-06-20T03:02:14Z)
• Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation [76.85274970052762]
  This paper is motivated by a proliferation of options of alignment regularizations. We evaluate the performances of several popular design choices along the dimensions of robustness and invariance. We also formally analyze the behavior of alignment regularization to complement our empirical study under assumptions we consider realistic.
  arXiv  Detail & Related papers  (2022-06-04T04:29:19Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• An AO-ADMM approach to constraining PARAFAC2 on all modes [6.3172660601651]
  We propose an algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM) We show that the proposed PARAFAC2 AO-ADMM approach allows for flexible constraints, recovers the underlying patterns accurately, and is computationally efficient compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-04T09:39:01Z)
• PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series [5.911865723926626]
  Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
  arXiv  Detail & Related papers  (2021-09-30T20:01:19Z)
• Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition [7.9603223299524535]
  This paper revisits conventional sequential modeling approaches, aiming to address the problem of capturing time-varying temporal dependency patterns. We propose a different formulation of HMMs, whereby the dependence on past frames is dynamically inferred from the data. We derive a tractable inference algorithm based on the forward-backward algorithm.
  arXiv  Detail & Related papers  (2020-02-13T23:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.