ctsfeatures R package analyzes categorical time series data.
problem Lack of tools for analyzing categorical time series data.
method Provides functions for extracting statistical features and constructing graphs.
result Package enables traditional machine learning tasks on categorical time series.
nTreeClus clusters categorical sequences using tree-based learners and k-mers.
problem Challenges in clustering categorical and sequential data.
method nTreeClus uses Tree-based Learners, k-mers, and autoregressive models for categorical time series.
result nTreeClus outperformed baseline methods in various validation metrics.
Three bootstrap tests compare categorical time series generating processes.
problem Testing equality of generating processes in categorical time series.
method Proposes three tests using dissimilarity measures and bootstrap methods.
result Advantages and disadvantages of each bootstrap method discussed.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
ForecastGAN improves multi-horizon time series forecasting by integrating numerical and categorical features.
problem Limited performance of existing approaches in short-term and long-term forecasting.
method Decomposition, model selection, adversarial training.
result ForecastGAN consistently outperforms state-of-the-art transformer models for short-term forecasting.
New method fits sparse Markov models to categorical time series using convex clustering.
problem Exponentially growing parameters in higher-order Markov chains.
method Convex clustering and regularization for parsimonious modeling.
result Theoretical consistency and finite sample performance demonstrated.
This study improves cryptocurrency price forecasting using time series categorization and deep learning.
problem Accurate prediction of cryptocurrency prices is challenging due to limited data and diverse behaviors.
method The approach involves categorizing financial time series, creating deep learning models for each category, and combining data from other cryptocurrencies to increase training data.
result The method increases prediction accuracy by learning each subseries category with similar behavior and combining data from other cryptocurrencies.
ERAPS builds prediction sets for time-series data.
problem Uncertainty quantification in complex machine learning methods for time-series data.
method ERAPS is an ensemble-based framework for constructing prediction sets for time-series data, allowing unknown dependencies within features and responses.
result ERAPS demonstrates valid marginal and conditional coverage and yields smaller prediction sets than competing methods.
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models. A data set of temporal event sequences can be represented as a data set of three-dimensional points, each point is defined by three varia…
Developed DLCM for more accurate clustering of categorical data.
problem Restrictive conditional independence assumption in traditional LCMs.
method Bayesian Dependent Latent Class Model (DLCM) that allows conditional dependence.
result DLCMs are effective in applications with time series, overlapping items, and structural zeroes.
Improved IDS using LSTM and embedding for network traffic data.
problem Network security threats and need for IDS to detect attacks.
method Proposes IDS models using LSTM for time-series and embedding for categorical network traffic data.
result Improves binary classification accuracy to 99.72%.
Improved time series classification with imputed data using label-guided forest-based methods.
problem Missing data in time series data.
method Label-guided imputation using forest-based proximity measures.
result Imputation leads to higher classification accuracies, even with imputed values differing from true values.
Multivariate time series is a very active topic in the research community and many machine learning tasks are being used in order to extract information from this type of data. However, in real-world problems data has missing values, which may difficult the application of machine learning techniques to extract informat…
This study examines how discretization improves neural forecasting models.
problem Improving predictive performance of neural forecasting models.
method Empirical investigation of data binning techniques on various neural forecasting architectures.
result Data binning almost always improves forecasting accuracy, but the type of binning is less important.
Paper establishes a comprehensive benchmark for ECG time-series analysis.
problem Incomplete understanding of ECG signal properties and limitations in evaluation metrics.
method Categorization of downstream applications, identification of limitations, introduction of a novel metric, benchmarking of time-series models.
result Validation of the effectiveness of the proposed metric and model architecture.
Study categorizes time series anomaly detection metrics based on evaluation challenges.
problem Challenges in evaluating time series anomaly detection due to diverse application objectives and metric assumptions.
method Problem-oriented framework categorizing metrics into six dimensions based on evaluation challenges.
result Quantifies each metric's discriminative ability and reveals limitations of widely used metrics.
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.
Paper studies Time Series Extrinsic Regression, improving on existing methods.
problem Learning relationship between time series and continuous scalar variables.
method Motivated and studied TSER, benchmarked TSC and ML algorithms.
result Rocket algorithm achieves highest overall accuracy in TSER.
This paper reviews self-supervised learning methods for time series anomaly detection.
problem Challenges in traditional unsupervised methods for time series anomaly detection.
method Self-supervised learning techniques for time series anomaly detection.
result Enhanced performance of anomaly detectors through self-supervised learning.
Unified taxonomy categorizes DL-based MTSAD methods.
problem Lack of systematization in MTSAD research.
method Two-fold approach: derived from methodological studies and reviewed papers.
result Convergence toward Transformer-based and reconstruction/prediction models.
PyPOTS simplifies machine learning on time series with missing data.
problem Handling missing data in time series analysis.
method Unified interface for imputation, forecasting, anomaly detection, classification, and clustering.
result Robust and scalable Python toolkit for multivariate partially-observed time series.
Unified discrete diffusion for categorical data simplifies training and sampling.
problem Training and sampling in discrete diffusion models for categorical data.
method Mathematical simplifications and elegant unification of discrete-time and continuous-time discrete diffusion.
result Unified Simplified Discrete Denoising Diffusion (USD3) outperforms SOTA baselines.
Financial time series prediction, especially with machine learning techniques, is an extensive field of study. In recent times, deep learning methods (especially time series analysis) have performed outstandingly for various industrial problems, with better prediction than machine learning methods. Moreover, many resea…
In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challengin…
This paper brings deep learning at the forefront of research into Time Series Classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state …
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new Divide and Combine based approach to do K means clust…
This work concludes a series of four papers on the foundational theory of orbifolds and stacks. We apply the abstract theory, developed in its predecessors, to orbifolds derived from manifolds. Specifically, we show how the very concrete topological base spaces associated to such orbifolds can be described and manipula…
Survey of diffusion models for time series forecasting.
problem Lack of systematic taxonomy for diffusion models in time series forecasting.
method Introduction and review of standard diffusion models, their variants, and their adaptation to time series tasks.
result Provides a comprehensive categorization and summary of diffusion models for time series forecasting.
MSIN model discovers relevant financial news for time series data.
problem Discovering relevant textual stories associated with numerical time series data.
method Joint learning of time series and text data using MSIN model.
result MSIN achieves up to 84.9% and 87.2% in recalling ground truth articles for two stock time series.
This review examines DL models for financial forecasting.
problem Lack of comprehensive reviews on DL for financial forecasting.
method Categorized studies by forecasting area and DL model type.
result DL models outperform traditional ML methods.
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to help classify and categorize the data. Traditional machine learning techniques for time-series data offer several solutions to solve these …
Second part of a series on higher coverings of racks and quandles.
problem Characterizing higher-dimensional centrality conditions in racks and quandles.
method Applying higher categorical Galois theory to racks and quandles.
result Identification and characterization of higher coverings, trivial coverings, and normal coverings.
A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data. SGLDS uses the Bernoulli-Poisson link together with a gamma process to generate an infinite dimensional sparse random graph to model state transitions. Depending on the sparsity pattern of…
We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals and learns to satisfy them. This permits the network to choose sub-sets of a problem which are most amenable to its abilities to focus on s…
There exist many approaches for description and recognition of unseen classes in datasets. Nevertheless, it becomes a challenging problem when we deal with multivariate time-series (MTS) (e.g., motion data), where we cannot apply the vectorial algorithms directly to the inputs. In this work, we propose a novel multiple…
Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions can be both categorical and continuous variables containing…
Method estimates exogenous and endogenous factors from event times.
problem Estimating factors influencing event occurrence.
method Combines inhomogeneous Poisson and Hawkes processes, fits using free energy minimization.
result Four regimes identified based on factor detection.
We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme. It models categorical-valued time series that exhibit dynamics from multiple underlying patterns (e.g. user behavior traces). We simplify the idea of capturing these patterns by hierarc…
Study embedding calculus and link invariants using functor calculus.
problem Detect Milnor invariants using embedding towers of string links.
method Use functor calculus and Goodwillie-Weiss embedding calculus.
result Embedding tower detects Milnor invariants.
CPML efficiently learns new metrics for categorical data.
problem Metric learning for categorical data.
method CPML (categorical projected metric learning) using Schatten p-norms.
result CPML provides efficient metric learning with improved accuracy.
A new method for optimizing models with categorical variables using diffusion.
problem Optimizing models with categorical variables, especially in discrete distributions.
method Introducing ReDGE, a diffusion-based soft reparameterization method for categorical distributions.
result ReDGE consistently matches or outperforms existing gradient-based methods in experiments.
Analyzes how economic policies affect wealth distribution in Bitcoin token economy.
problem Impact of economic policies on wealth distribution in token economies.
method Eliminated noise in wealth distribution data using macroeconomic and microeconomic time series. Causality analysis between BIPs and wealth distribution data.
result Proposed a structure for economic policy taxonomy in token economies.
Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic informatio…
Paper finds linear laws in Bitcoin price changes, aiding anomaly detection.
problem Detecting anomalies in Bitcoin price changes.
method Time embedding of autocorrelation function, binary series generation, stepped time windows.
result Linear laws became more complex before major market events, suggesting price manipulation.
The paper defines and studies the category of Z-graded manifolds, including their intrinsic structure and formal properties.
problem Understanding the categorical properties and intrinsic structure of Z-graded manifolds.
method Describing local models, explaining formality, and formulating analogues of theorems.
result Proper definitions of objects and morphisms in the category of Z-graded manifolds, and formulation of Batchelor's theorem.
Unified review of methods for inferring non-stationary process parameters.
problem Inferring parameters of non-stationary processes without a known model.
method Unified review and categorization of algorithms for Parameter Inference from a Non-stationary Unknown Process (PINUP).
result Simple statistical features can perform well on non-stationary systems, highlighting gaps in existing methods.
Random Intersection Chains selects important interactions from categorical features.
problem Heavy computational burden in considering all interactions for categorical features.
method Randomly generates chains of intersections, estimates and selects frequent patterns.
result Selected patterns are the most frequent in the data set.
Local-HDP learns independent topics for each 3D object category in real-time.
problem Learning independent topics for each 3D object category in real-time.
method Local-Hierarchical Dirichlet Process (Local-HDP) with online variational inference.
result Local-HDP outperforms other approaches in accuracy, scalability, and memory efficiency.