VSDN models sporadic time series with neural SDEs.
problem Modeling irregular and sparse time series data.
method Variational Bayesian method and neural SDEs.
result VSDNs outperform state-of-the-art models in prediction and interpolation.
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48 results for “sporadic time series”
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurren…
The paper classifies and proves properties of symmetry breaking operators for specific groups.
problem Classifying and understanding symmetry breaking operators for de Sitter and Lorentz groups.
method Constructing and classifying differential symmetry breaking operators, proving localness, and showing sporadic nature.
result All symmetry breaking operators are differential and sporadic, not obtainable by residue formulas.
CARRNN tackles deep learning for sporadic data, improving prediction errors in healthcare.
problem Challenges in learning temporal patterns from sporadic multivariate longitudinal data.
method Developed a novel deep learning architecture combining RNN and CAR models, using a generalized discrete-time autoregressive model.
result CARRNN achieves the lowest prediction errors in multivariate time-series regression tasks.
EDICT learns evidential distributions for irregular time series, improving predictions and uncertainty quantification.
problem Challenges in predicting and characterizing uncertainty for irregular time series data.
method EDICT (Evidential Distributions for Irregular Time Series) learns a continuous-time evidential distribution.
result EDICT achieves competitive performance on time series classification tasks and provides better uncertainty quantification.
HERMES model predicts nonstationary fashion trends using social media data.
problem Forecasting nonstationary fashion time series for optimal inventory decisions.
method Hybrid model combining parametric models, seasonal components, and recurrent neural networks with external signals.
result State-of-the-art results on fashion dataset and M4 competition time series.
The goal of this paper is to give a conjectural census of complex hyperbolic sporadic groups. We prove that only finitely many of these sporadic groups are lattices. We also give a conjectural list of all lattices among sporadic groups, and for each group in the list we give a conjectural presentation, as well as a lis…
Study optimal portfolio strategy with sporadic bankruptcy for isoelastic utility.
problem Maximizing expected isoelastic utility in a stock with potential bankruptcy.
method Coupled Hamilton-Jacobi-Bellman (HJB) equations, stochastic integral approach.
result Non-myopic optimal weights for non-logarithmic utilities.
Novel imputation method for EHRs with structured and sporadic missingness.
problem Missing data in integrated EHR datasets for clinical applications.
method Macomss, a novel imputation framework for structurally and heterogeneously missing data.
result Macomss outperforms existing methods in imputation and downstream prediction accuracy.
Tangential families are 1-parameter families of rays emanating tangentially from smooth curves. We classify tangential family germs up to Left-Right equivalence: we prove that there are two infinite series and four sporadic simple singularities of tangential family germs (in addition to two stable singularities). We gi…
New framework for reinforcement learning with sporadic state observations.
problem Partial observability in reinforcement learning.
method Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs).
result Optimistic algorithm achieving regret bound for episodic learning.
New algorithm recovers sparse signals from linearly sparse dictionaries efficiently.
problem Recovering sparse signals from linearly sparse dictionaries.
method Spectral method on reweighted covariance matrices.
result First polynomial-time algorithm for near-linear sparsity in overcomplete dictionaries.
The study identifies and predicts extreme stock price fluctuations using HHT and SVM.
problem Sporadic large stock price fluctuations due to various factors.
method Hilbert-Huang Transformation (HHT) for identifying extreme events (EEs) and Support Vector Regression (SVR) for forecasting.
result High instantaneous energy concentration in stock price during both positive and negative extreme events.
We study the action of the mapping class group M(F) on the complex of curves of a non-orientable surface F. We obtain, by using a result of K. S. Brown, a presentation for M(F) defined in terms of the mapping class groups of the complementary surfaces of collections of curves, provided that F is not sporadic, i.e. the …
The Wythoff construction takes a -dimensional polytope , a subset of and returns another -dimensional polytope . If is a regular polytope, then is vertex-transitive. This construction builds a large part of the Archimedean polytopes and tilings in dimension 3 and 4. We want …
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between renewal processes, and a collection of current models used for intermittent demand forecasting. We then develop a set of models that benefit…
We first show that every quasisimple sporadic group possesses an unmixed strongly real Beauville structure aside from the Mathieu groups M11 and M23 (and possibly 2B and M). We go on to show that no almost simple sporadic group possesses a mixed Beauville structure. We then go on to use the exceptional nature of the al…
As video games attract more and more players, the major challenge for game studios is to retain them. We present a deep behavioral analysis of churn (game abandonment) and what we called "purchase churn" (the transition from paying to non-paying user). A series of churning behavior profiles are identified, which allows…
Divide knots and links, defined by A'Campo in the singularity theory of complex curves, is a method to present knots or links by real plane curves. The present paper is a continuation of the author's previous result that every knot in the major subfamilies of Berge's lens space surgery (i.e., knots yielding a lens spac…
The strong symmetric genus of a group is the smallest genus of a surface on which the group acts faithfully as a group of orientation preserving automorphisms. In this paper we announce and prove the strong symmetric genus for the hyperoctahedral groups. This parameter is already known for the alternating and symmetric…
New condition prevents hyperbolic spaces from matching curve complexes.
problem Identifying when hyperbolic spaces cannot match curve complexes.
method Analyzing specific hyperbolic complexes and identifying a condition.
result Identified a condition preventing quasi-isometry between hyperbolic spaces and curve complexes.
It is experimentally known that achiral hyperbolic 3-manifolds are quite sporadic at least among those with small volume, while we can find plenty of them as amphicheiral knot complements in the 3-sphere. In this paper, we show that there exist infinitely many achiral 1-cusped hyperbolic 3-manifolds not homeomorphic to…
New partial solution to Hurwitz problem for surface branched covers.
problem Existence of surface branched covers with specific partition lengths.
method Inductive proof on genus and branching points, excluding technology of constellations.
result Identification of exceptional candidate data and their properties.
The paper is on the vanishing topology of singular Milnor fibres of holomorphic families of arbitrary square, symmetric and skew-symmetric matrices with sufficiently many parameters. We define vanishing cycles on such fibres, prove an extended form of the Damon-Pike conjecture about the families of a special type…
Smooth surfaces in simply connected 4-manifolds yield groups with non-trivial homology.
problem Finding groups with non-trivial fundamental groups in the complements of surfaces in 4-manifolds.
method Combining topological constructions with homological properties of simple groups.
result The complement of a surface in a simply connected 4-manifold can have a non-trivial fundamental group.
Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the disco…
Researchers prove the automorphism group of a sphere complex matches the mapping group of a graph.
problem Understanding the automorphisms of sphere complexes associated with graphs.
method Analyzing the group of proper homotopy equivalences and constructing an exhaustion of the sphere complex.
result The automorphism group of the sphere complex is isomorphic to the mapping group of the graph.
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable compar…
Volatility of Linear and Nonlinear Time Seriescond-mat.stat-mech
Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, , can be detected and quantified by studying the correlations in the magnitude series , i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact …
Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two cla…
New projection complex shows some surface homeomorphisms have positive commutator length.
problem Understanding commutator length in surface homeomorphisms.
method Constructing unbounded quasi-trees and a new projection complex.
result Some surface homeomorphisms have positive stable commutator length.
Modeling regime shifts in co-evolving time series with interactions and time-dependency.
problem Discovering and modeling regime shifts in multiple time series with relationships and time-dependent behaviors.
method Modeling interactions and time-dependency in co-evolving time series using a mapping grid and dynamic network representation for regime identification and time-dependent Cox regression for regime transition probabilities.
result A principled approach for modeling interactions and time-dependency in co-evolving time series.
MDF represents time series motifs as images for improved classification.
problem Classifying time series data with high-order patterns.
method Motif Difference Field (MDF) using Fully Convolutional Networks (FCN).
result MDF outperforms other methods on UCR time series datasets.
We provide the proof that the space of time series data is a Kolmogorov space with -separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes fro…
Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through s…
Overview of high-dimensional time series regression methods.
problem Estimation and inference with high-dimensional time series data.
method Limit theory for high-dimensional dependent data, asymptotic theory for time series regression, statistical learning methods.
result Main limit theory results and asymptotic theory for high-dimensional time series regression.
We investigate polyhedral -manifolds as subcomplexes of the boundary complex of a regular polytope. We call such a subcomplex {\it -Hamiltonian} if it contains the full -skeleton of the polytope. Since the case of the cube is well known and since the case of a simplex was also previously studied (these are so…
New method uses Transformers for flu forecasting.
problem Forecasting influenza-like illness trends.
method Transformer-based machine learning models with self-attention.
result Forecasting results are competitive with state-of-the-art methods.
Improved prediction of hierarchical time series using structured regularization.
problem Making coherent forecasts for hierarchical time series.
method Structured regularization method for bottom-level time series predictions.
result Superior prediction accuracy and computational efficiency compared to previous methods.
Introduces a new benchmark for time series extrinsic regression.
problem Predicting a single continuous value from univariate or multivariate time series, not necessarily related to the predictor.
method Developed a new benchmarking archive for time series extrinsic regression.
result Initial benchmarking of existing models on the new TSER datasets.
Prescribed Arc Graphsmath.GT
Defined a new graph type for compact surfaces, proving its connectedness and infinite diameter.
problem Understanding the structure of arc graphs on compact surfaces.
method Defining and analyzing the prescribed arc graph for compact surfaces with boundary and relations .
result The prescribed arc graph is connected and infinite-diameter, with specific conditions for Gromov hyperbolicity.
Few-shot learning improves time-series forecasting with limited data.
problem Limited data in target tasks degrade forecasting performance.
method A few-shot learning method using recurrent neural networks with attention.
result The model forecasts future values effectively with minimal data.
Meta-learning for Koopman spectral analysis with short time-series data.
problem Lack of long time-series for training embedding functions in Koopman spectral analysis.
method Meta-learning approach using bidirectional LSTM and neural network to estimate embedding functions from short time-series.
result The proposed method achieves better performance in eigenvalue estimation and future prediction compared to existing methods.
Transformers improve time series modeling by capturing long-range dependencies.
problem Capturing long-range dependencies in time series data.
method Summarized and reviewed adaptations of Transformers for time series analysis.
result Transformers enhance time series forecasting, anomaly detection, and classification.
Archive of 20 time series datasets for forecasting evaluation.
problem Lack of comprehensive time series forecasting datasets.
method Compilation and characterisation of 20 datasets from various domains.
result Characterisation and performance evaluation of datasets.
Multidimensional time series are sequences of real valued vectors. They occur in different areas, for example handwritten characters, GPS tracking, and gestures of modern virtual reality motion controllers. Within these areas, a common task is to search for similar time series. Dynamic Time Warping (DTW) is a common di…
theft package simplifies feature extraction for time series analysis in R.
problem Lack of a unified access point and methodological pipelines for feature-based time series analysis.
method theft package provides a unified framework for computing features from six open-source time series feature sets.
result theft enables comprehensive quantification and interpretation of time series structure.
Method summarizes and predicts time series data for COVID-19 cases and deaths.
problem Summarizing and predicting time series data for multiple related time series.
method Hierarchical algorithm generating shapelets for centroids, nearest neighbor search for labeling, dynamic time warping for non-uniform lengths.
result Predictive model for individual time series based on aggregated statistics.