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.
This study improves estimation of locally stationary functional time series using NW method.
problem Accurately capturing time-dependence in locally stationary functional time series with time-varying covariates.
method Nadaraya-Watson (NW) estimation procedure for the conditional distribution of LSFTS.
result Established convergence rates of NW estimator for LSFTS with respect to Wasserstein distance.
AR model forecasts partially observed dynamical time series by estimating evolution function and imputing missing variables.
problem Forecasting dynamical time series with missing variables.
method Autoregressive with slack time series (ARS) model.
result ARS model forecasts future time series with time-invariant and linear assumptions.
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
problem Forecasting and uncertainty quantification for Sea Level Anomalies.
method Functional data analysis, Conformal Prediction, Functional Autoregressive Processes.
result Proposed method provides accurate probabilistic predictions and uncertainty quantification for Sea Level Anomalies.
Bayesian method tests Granger causality in functional time series.
problem Testing Granger causality between functional time series.
method Bayesian dynamic linear models (DLM) and Bayes Factor.
result Captures Granger causality between yield curves and weather conditions.
This short note suggests a heuristic method for detecting the dependence of random time series that can be used in the case when this dependence is relatively weak and such that the traditional methods are not effective. The method requires to compare some special functionals on the sample characteristic functions with…
Proposes a new model for non-linear regression of multivariate time series data.
problem Regression models for non-scalar variables, especially time series, have limitations.
method Develops a non-linear function-on-function model using neural networks.
result Demonstrates effectiveness through real-world applications.
Dynamic functional time-series methods improve forecast accuracy for foreign exchange implied volatility surfaces.
problem Forecasting implied volatility surfaces in foreign exchange markets.
method Dynamic functional principal component analysis and multivariate functional time-series methods.
result Dynamic univariate functional time-series method shows the greatest improvement in forecast accuracy.
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series…
DF2M uses deep neural networks within a factor model for high-dimensional functional time series forecasting.
problem Forecasting high-dimensional functional time series with explainability and accuracy.
method Bayesian nonparametric model based on Indian Buffet Process and multi-task Gaussian Process, incorporating a deep kernel function.
result DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models.
RST improves environmental time series classification accuracy using randomized B-spline trees.
problem Improving accuracy in classifying complex environmental time series.
method Randomized Spline Trees (RST) integrates randomized functional representations into ensemble learning.
result RST variants outperform standard Random Forests and Gradient Boosting on most environmental time series datasets.
A method for noise reduction in functional time series using FPCA.
problem Noise contamination in functional time series.
method Extending FPCA to separate signal and noise components.
result Optimal projection minimizes mean integrated squared error.
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.
Novel method converts time series data into functional data for high dimensional classification.
problem Small sample size problem in high dimensional time series data.
method Classwise Functional Principal Component Analysis (PCA) followed by Bayesian linear classifier.
result Demonstrated efficacy on synthetic and real data sets.
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…
DDD reformulated for sparse matrices, integrating trajectory and snapshot time series data.
problem Efficiently integrate trajectory and snapshot time series data.
method Reformulate DDD to use compact basis functions, reducing parameter scaling.
result Inference of sparse matrices reduces the number of parameters in DDD.
Two new methods improve forecasting of functional time series data.
problem Forecasting of functional time-dependent data.
method Functional Singular Spectrum Analysis (FSFA) based forecasting methods.
result Our methods outperform existing algorithms for periodic stochastic processes.
This paper evaluates anomaly detection methods for multivariate time series data.
problem Lack of systematic comparison of anomaly detection methods on multivariate time series data.
method Comprehensive evaluation of 10 models and 4 scoring functions on 10 datasets.
result Dynamic scoring functions outperform static ones, and the choice of scoring functions matters more than the model choice.
The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.
problem Challenges in capturing nonlinear dynamics from noisy time series data.
method A projected nonlinear state-space model with kernel functions applied to projected lines.
result The model effectively learns and forecasts complex nonlinear dynamics with computational efficiency.
Two methods forecast functional time series, offering competitive results.
problem Forecasting functional time series with model-free approaches.
method Two nonparametric methods: k-nearest neighbors adaptation and curve envelope selection.
result Competitive results with and often superior to benchmarks.
A new method uses sinusoidal functions to represent timestamps as dense vectors for improving irregularly sampled time series learning.
problem Challenges in supervised learning with irregularly sampled time series due to irregular time intervals.
method Proposes a novel method to represent timestamps as dense vectors using sinusoidal functions, called Time Embeddings.
result Improves LSTM-based and classical machine learning models, especially with very irregular data.
R package otsfeatures analyzes ordinal time series data.
problem Lack of tools for analyzing ordinal time series data.
method Provides functions for extracting statistical features and performing inferential tasks.
result Package enables traditional machine learning tasks on ordinal time series.
Model predicts competition between similar products in sales.
problem Predicting cannibalization between similar products in sales.
method Developed a neural network model that computes a 'competitiveness' function based on product features.
result The model outperforms traditional methods in predicting market share.
Paper detects and estimates breaks in high-dimensional functional time series.
problem Detecting and estimating structural breaks in heterogeneous mean functions of high-dimensional functional time series.
method Proposes a new test statistic combining functional CUSUM and power enhancement components, with a clustering algorithm for group structure estimation.
result The proposed techniques have satisfactory performance in finite samples, detecting and estimating breaks effectively.
In this paper, we propose a technique for time series clustering using community detection in complex networks. Firstly, we present a method to transform a set of time series into a network using different distance functions, where each time series is represented by a vertex and the most similar ones are connected. The…
NeuTSFlow models continuous functions behind time series forecasting.
problem Forecasting treats time series as discrete sequences, ignoring their continuous nature.
method NeuTSFlow uses Neural Operators to learn the transition between historical and future function families.
result NeuTSFlow outperforms traditional methods in forecasting accuracy and robustness.
The study uses Gaussian Processes with Tweedie likelihood for forecasting intermittent time series.
problem Forecasting intermittent time series with high accuracy and flexibility.
method The approach combines Gaussian Processes with two forecast distributions: negative binomial and Tweedie.
result TweedieGP provides better probabilistic forecasts, especially for high quantiles.
BrainCast predicts whole-brain fMRI time series from short scans.
problem Short scans reduce fMRI data quality and statistical power.
method Spatio-temporal forecasting framework for fMRI time series.
result BrainCast improves fMRI time series quality and prediction.
Estimates impulse response functions using machine learning in time series data.
problem Estimating causal effects of discrete treatments over time with flexible models.
method Double/debiased machine learning for nonparametric time series data.
result Consistent and asymptotically normal estimator for impulse response functions.
Paper develops methods for inference on time series data using neural networks and sieves.
problem Inference on time series data with nonparametric conditional moment restrictions.
method GN-QLR based inference using general nonlinear sieves and multilayer neural networks.
result Optimally weighted GN-QLR statistic is asymptotically Chi-square distributed.
CATS enhances MTSF by generating ATS from OTS to improve forecasting accuracy.
problem Recent deep learning models often outperform multivariate ones in MTSF.
method CATS constructs ATS from OTS using a 2D temporal-contextual attention mechanism.
result CATS achieves state-of-the-art performance with reduced complexity.
We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically so…
We consider strictly stationary heavy tailed time series whose finite-dimensional exponent measures are concentrated on axes, and hence their extremal properties cannot be tackled using classical multivariate regular variation that is suitable for time series with extremal dependence. We recover relevant information ab…
New methods for handling time-varying label noise in time series classification.
problem Temporal label noise in time series classification tasks.
method Proposed methods to estimate temporal label noise function directly from data.
result Our methods lead to state-of-the-art performance under diverse types of temporal label noise.
New method recovers causal networks from short time-series data.
problem Inferring causal relationships from short time-series data in complex systems.
method Large-scale Nonlinear Granger Causality (lsNGC) approach.
result Captures meaningful interactions from limited observational data.
Proves and tests methods for learning time-series with breaks.
problem Learning time-series with structural breaks.
method Complete proofs and experimental validation of a regularized loss function.
result Experimental results support the validity of the techniques.
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.
Investigates chaotic financial time series with monthly contributions and devaluation.
problem Analyzing chaotic behavior in financial processes with piecewise contributions and negative interest rates.
method Examines a financial process with monthly contributions and devaluation, showing dichotomy in behavior.
result Financial time series exhibit either periodic sequences or Cantor set of ω-limit points, with chaotic behavior at points of a Cantor attractor.
Variant of mSSA improves time series prediction error.
problem Improve prediction error in multivariate time series.
method Introduce spatio-temporal factor model, establish prediction error scaling.
result Prediction error scales as 1 / √(min(N, T)T).
BayOTIDE tackles imputation of irregularly sampled multivariate time series with uncertainty quantification.
problem Imputation of irregularly sampled multivariate time series with missing values and noises.
method BayOTIDE treats multivariate time series as a combination of low-rank temporal factors with different patterns, using Gaussian Processes (GPs) as functional priors and converting them into state-space priors for scalable online inference.
result BayOTIDE can handle imputation over arbitrary time stamps and offers uncertainty quantification and interpretability.
Methodology to measure lag relevance in time series models.
problem Measuring lag relevance in machine learning models for univariate time series.
method Ghost variables, Shapley values, additive importance measures, auto-relevance and partial auto-relevance functions, one-step forecast.
result Calculated relevance measures successfully demonstrate expected lag structure in almost all cases.
NFM models time-series data directly in the Fourier domain, achieving state-of-the-art performance.
problem Traditional time-series analysis focuses on the time domain, limiting flexibility.
method NFM models time-series data in the Fourier domain, using frequency extrapolation and interpolation.
result NFM achieves state-of-the-art performance on various time-series tasks.
Online learning rbfnet improves multi-horizon returns forecasts for financial time series.
problem Nonstationarity and concept drift in financial time series.
method Combines feature representation transfer with sequential optimisation.
result Online learning rbfnet outperforms random-walk and batch learners.
Generative model for time series using Schrödinger bridge.
problem Creating synthetic time series data with temporal dynamics.
method Schrödinger bridge approach for entropic interpolation via optimal transport.
result The method generates synthetic time series that respect temporal dynamics.
Geometric pruning rules improve change point detection in multiple time series.
problem Detecting multiple changes in multiple independent time series.
method Dynamic programming algorithms with inequality-based and geometric pruning rules.
result Geometric pruning rules offer close-to-linear time complexity for multiple independent time series.
tsflex speeds up time series processing and feature extraction.
problem Inefficient and inflexible time series processing tools.
method Flexible Python toolkit exttttsflex for multivariate, asynchronous time series. result Significantly faster and more memory-efficient than existing packages.
Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequ…
Paper introduces novel distances for clustering ordinal time series.
problem Clustering ordinal time series with discrete response.
method Introduces two novel distances and fuzzy clustering algorithms.
result Fuzzy clustering algorithms accurately group series from similar stochastic processes.