arXiv research

Unified framework for generating meteorological time series from text.

Previous studies indicate that nonlinear properties of Gaussian time series with long-range correlations, $u_i$, can be detected and quantified by studying the correlations in the magnitude series $|u_i|$, i.e., the ``volatility''. However, the origin for this empirical observation still remains unclear, and the exact …

Lie-Butcher (LB) series are formal power series expressed in terms of trees and forests. On the geometric side LB-series generalizes classical B-series from Euclidean spaces to Lie groups and homogeneous manifolds. On the algebraic side, B-series are based on pre-Lie algebras and the Butcher-Connes-Kreimer Hopf algebra…

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…

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…

Study series invariants of plumbed 3-manifolds using root lattices.

New formula and properties of inverted Habiro series derived from GM series.

Paper introduces novel distances for clustering ordinal time series.

MPPN network improves long-term time series forecasting accuracy.

Overview of high-dimensional time series regression methods.

Archive of 20 time series datasets for forecasting evaluation.

Improved prediction of hierarchical time series using structured regularization.

New kernel handles irregularly-spaced multivariate time series.

Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif diffe…

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…

Modeling regime shifts in co-evolving time series with interactions and time-dependency.

Introduces a new benchmark for time series extrinsic regression.

In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs …

Few-shot learning improves time-series forecasting with limited data.

Meta-learning for Koopman spectral analysis with short time-series data.

Transformers improve time series modeling by capturing long-range dependencies.

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-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…

Global models outperform univariate benchmarks in complex time series forecasting.

theft package simplifies feature extraction for time series analysis in R.

CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.

Study q-series for 3-manifolds with line defects, proving homomorphism and conjecturing holomorphic modularity.

Quantum modularity proven for specific theta series.

A hybrid loss framework improves time series forecasting by balancing global and component errors.

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…

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on …

New deep probabilistic model handles missing data in time series forecasting.

Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; w…

A new framework for generating predictive features in noisy multivariate time series.

Bayesian QFSTS model tackles feature selection in quantile time series analysis.

HopCPT improves conformal prediction for time series with temporal dependencies.

Method summarizes and predicts time series data for COVID-19 cases and deaths.

Proposes a test for shared information between time series and events.

New series invariant for knots and cables, with robustness and relations.

New q-series found for osp(2|2n) Lie superalgebra.

The Poincaré series for surfaces with boundary extends to the complex plane.

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…

AR model forecasts partially observed dynamical time series by estimating evolution function and imputing missing variables.

Survey on LLMs for time series analytics across various domains.

Study improves GFM accuracy with time series augmentation.

FinTSBridge evaluates financial time series models for asset pricing.

Quantum model generates financial data with fewer parameters.

This paper presents a novel time series clustering method, the self-organising eigenspace map (SOEM), based on a generalisation of the well-known self-organising feature map (SOFM). The SOEM operates on the eigenspaces of the embedded covariance structures of time series which are related directly to modes in those tim…

Paper studies Time Series Extrinsic Regression, improving on existing methods.