arXiv research

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…

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

NEMoTS improves time series analysis by deriving efficient, interpretable models.

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

Properties of low-variability periods in the time series are analysed. The theoretical approach is used to show the relationship between the multi-scaling of low-variability periods and multi-affinity of the time series. It is shown that this technically simple method is capable of reveling more details about time-seri…

Overview of high-dimensional time series regression methods.

New method detects intrinsic cross-correlations in non-stationary time series affected by common factors.

Time series analysis is a key component of machine learning, with applications in various fields.

TADA detects anomalies in time series using topological data analysis.

Paper shows how SFA fits into FBM framework for time series separation.

In the work, a comparative correlation and fractal analysis of time series of Bitcoin crypto currency rate and community activities in social networks associated with Bitcoin was conducted. A significant correlation between the Bitcoin rate and the community activities was detected. Time series fractal analysis indicat…

The paper reviews Hankel low-rank methods for time series analysis and forecasting.

Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this pap…

This paper describes a new approach to time series modeling that combines subject-matter knowledge of the system dynamics with statistical techniques in time series analysis and regression. Applications to American option pricing and the Canadian lynx data are given to illustrate this approach.

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

With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-se…

VTA combines verbal and latent reasoning for accurate stock time-series forecasts.

A new framework for time series analysis using state-space learning.

OneShotSTL efficiently decomposes time series online, improving speed and accuracy.

Paper establishes a comprehensive benchmark for ECG time-series analysis.

In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…

Paper uses topological data analysis for time series classification.

Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originat…

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 …

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

Novel method converts time series data into functional data for high dimensional classification.

Analyzes Indian commercial dynamism using time series data.

Bayesian analysis of financial time series using R-INLA.

The multifractal detrended fluctuation analysis technique is employed to analyze the time series of gold consumer price index (CPI) and the market trend of three world's highest gold consuming countries, namely China, India and Turkey for the period: 1993-July 2013. Various multifractal variables, such as the generaliz…

This paper extends hypergraph construction to multivariate time series using signature transforms.

Researchers forecast VoIP traffic in mobile networks using multivariate time series analysis.

Survey categorizes time series anomaly detection methods.

Causal analysis predicts market trends using time series data.

The analysis of nonstationary time series is of great importance in many scientific fields such as physics and neuroscience. In recent years, Gaussian process regression has attracted substantial attention as a robust and powerful method for analyzing time series. In this paper, we introduce a new framework for analyzi…

New method phenotypes sleep apnea patients using time series analysis.

The level crossing and inverse statistics analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, $ν_α^+$, where $T_α =1/ν_α^+$ is the average waiting time for observing the level $α$ again. We estimate the probability $P(K, α)$, which provides us the probab…

Study compares Bitcoin, gold, and gas price complexity using multifractal and multiscale entropy methods.

Two possible definitions of fixed points in the self-similar analysis of time series are considered. One definition is based on the minimal-difference condition and another, on a simple averaging. From studying stock market time series, one may conclude that these two definitions are practically equivalent. A forecast …

The paper introduces a method for interpretable principal component analysis of high-dimensional time series.

New methods improve cross-correlation analysis of time series data.

This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal tra…

We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension $d$ to scale with the series length $T$. We treat the transition matrix of time series as a nuisance parameter and directly apply sparse principal component…

RST improves environmental time series classification accuracy using randomized B-spline trees.

It becomes increasingly popular to perform mediation analysis for complex data from sophisticated experimental studies. In this paper, we present Granger Mediation Analysis (GMA), a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imagin…

Variant of mSSA improves time series prediction error.

Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena in the frequency domain. The quantile periodogram is constructed from trigonometric quantile regression of time series data at different frequencie…

New clustering algorithm for time series data using RNN and variational Bayes.

NFM models time-series data directly in the Fourier domain, achieving state-of-the-art performance.