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.
ExpCLR uses expert features to improve time-series representation learning.
problem Current representation learning approaches fail to ensure useful properties for time-series data.
method ExpCLR employs expert features to replace data transformations in contrastive learning, ensuring two useful properties for time-series representations.
result ExpCLR outperforms state-of-the-art methods on three real-world time-series datasets.
SoftCLT improves time series representation learning by soft contrastive loss.
problem Ignoring inherent correlations in time series leads to poor representation quality.
method SoftCLT introduces instance-wise and temporal contrastive loss with soft assignments.
result SoftCLT consistently improves various downstream tasks in time series learning.
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.
Proposes a new approach to time series representation learning by embedding patches independently.
problem Capturing dependencies between time series patches is not optimal for representation learning.
method 1) Patch reconstruction task, 2) Patch-wise MLP, 3) Complementary contrastive learning.
result Improves time series forecasting and classification performance compared to state-of-the-art models.
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.
Quantum model generates complex time series data with preserved temporal dynamics.
problem Generating synthetic time series data with temporal correlations.
method Quantum Hamiltonian learning to encode temporal dynamics.
result The proposed quantum model captures unique temporal features of the learned time series.
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 …
Survey of deep learning methods for time series forecasting.
problem Improving accuracy in time series predictions across various domains.
method Analysis of common encoder and decoder designs, hybrid models, and decision support.
result Advancements in deep learning for time series forecasting.
Safe active learning for time-series models with Gaussian processes.
problem Learning time-series models while respecting safety constraints.
method Employing Gaussian processes with a nonlinear exogenous input structure, the approach dynamically explores the input space to generate data for model learning.
result The approach effectively learns time-series models under safety constraints, as demonstrated in a technical application.
Time series analysis is a key component of machine learning, with applications in various fields.
problem Time series analysis in machine learning
method Basic concepts, classical statistical models, modern machine learning approaches
result Machine learning techniques for time series analysis
Survey of data augmentation methods for improving deep learning on time series data.
problem Limited labeled data in real-world time series applications.
method Review and comparison of data augmentation methods for time series.
result Empirical comparison of data augmentation methods for various time series tasks.
Curriculum learning and imitation learning improve control over financial time-series data.
problem Improving control performance over complex financial time-series data.
method Data augmentation for curriculum learning and policy distillation for imitation learning.
result Curriculum learning shows significant improvement over time-series control tasks.
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series classification, many of which can be solved by reducing them to relate…
DeepLINK-T uses deep learning and knockoffs for time series data.
problem Interpreting and reproducible deep learning models for high-dimensional time series data.
method Combines deep learning with knockoffs for FDR control in feature selection for time series models.
result DeepLINK-T effectively controls FDR while demonstrating superior feature selection for high-dimensional longitudinal time series data.
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.
Generative model for inferring graph from time series data.
problem Generating graphs conditioned on multivariate time series data.
method Time Series Conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN).
result Demonstrates effectiveness and generalizability of TSGG-GAN on synthetic and real-world datasets.
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.
Deep learning autoencoder model clusters unlabeled time series data.
problem Clustering unlabeled time series data.
method Two-stage approach: create labels from time series characteristics, then use autoencoder for clustering.
result 87.5% accuracy in clustering unseen time series data.
Meta-GLAR combines global deep representations with local adaptation for improved forecasting accuracy.
problem Joint learning from related time series boosts accuracy but fails for out-of-sample forecasting.
method Meta-GLAR uses a meta-learning approach to adapt RNN representations for each time series.
result Meta-GLAR outperforms state-of-the-art methods in out-of-sample forecasting accuracy.
Develops framework for understanding deep learning in time series data.
problem Understanding and explaining decisions made by deep learning models in time series data.
method Uses deep neural networks to capture and explain temporal dependencies in time series data.
result Framework successfully captures and explains temporal dependencies in various synthetic and real-world datasets.
A new LSTM architecture improves time series forecasting efficiency.
problem Efficiency and accuracy in time series forecasting using linear models.
method Attention-free LSTM architecture for time series prediction.
result Improved prediction capacity and efficiency compared to LSTM.
This work proposes a method to learn graph structure for multivariate time series forecasting.
problem Improving multivariate time series forecasting by leveraging pairwise information.
method Learning a probabilistic graph model through optimizing mean performance over graph distribution parameterized by a neural network.
result Our method outperforms existing approaches in simplicity, efficiency, and performance.
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.
TNC learns time series representations by leveraging temporal neighborhoods.
problem Complex, unlabeled time series data.
method Temporal Neighborhood Coding (TNC) with a debiased contrastive objective.
result TNC outperforms other unsupervised methods in time series clustering and classification.
Paper analyzes Nyström regularization for time series forecasting with sequential sub-sampling.
problem Learning rate analysis of Nyström regularization for τ-mixing time series. method Banach-valued Bernstein inequality and integral operator approach for τ-mixing sequences. result Almost optimal learning rates for Nyström regularization with sequential sub-sampling.
Paper benchmarks CF mitigation in federated time series forecasting.
problem Catastrophic forgetting in federated learning for time series forecasting.
method Comprehensive evaluation of CF mitigation strategies in federated time series forecasting.
result Introduction of a new benchmark for CF in time series federated learning.
A new method for generating synthetic time series improves forecasting model accuracy.
problem Training forecasting models on imbalanced time series datasets.
method Data augmentation using oversampling strategies for imbalanced learning.
result The proposed method outperforms global and local models.
This paper studies concept drift detectors for financial time series.
problem Improving accuracy on financial time series with concept drifts.
method Three simple concept drift detectors tailored to financial time series.
result Two of the detectors are as effective as state-of-the-art detectors.
This research evaluates measures of dependence for financial time-series data.
problem Accurately preparing time series data and selecting an appropriate measure of dependence is challenging.
method Review and establishment of a comprehensive analysis framework for shaping time-series data and evaluating measures of dependence.
result A method, framework, and example for selecting and evaluating a suitable measure of dependence are presented.
Deep learning improves time series classification accuracy.
problem Classifying time series data efficiently.
method Developed deep neural networks for time series classification.
result Demonstrated superior performance of deep learning methods.
SigTime learns interpretable signatures from time series data.
problem Discovering meaningful patterns in time series data with high complexity and limited interpretability.
method Jointly trains two Transformer models using shapelet-based and feature engineering representations.
result Learned shapelets serve as interpretable signatures for time series classification.
VQShape learns interpretable time-series representations and achieves comparable performance to specialist models.
problem Lack of interpretability in existing time-series models.
method Vector quantization of time-series data into abstracted shapes.
result VQShape achieves comparable performance to specialist models in classification tasks.
Boosted embeddings improve time series forecasting models.
problem Improving time series forecasting accuracy.
method Gradient boosting with DNN weak learners and incremental weight updates.
result DeepGB model outperforms state-of-the-art models.
Paper tackles missing data in irregularly-sampled time series.
problem Modeling irregularly-sampled time series data.
method Encoder-decoder framework based on variational autoencoders and generative adversarial networks.
result Models achieve competitive or better classification results on irregularly-sampled multivariate time series.
Merlion is a machine learning library for time series tasks.
problem Anomaly detection and forecasting on time series data.
method Unified interface for models, pre/post-processing layers, AutoML, model ensembling, live deployment simulation.
result Benchmark numbers across different models and ensembles.
GANs can learn stylized facts of financial time series, but performance varies by architecture.
problem Capturing stylized facts of financial time series using GANs.
method Examination of GANs' ability to learn stylized facts of financial time series, focusing on univariate and multivariate data.
result GANs can capture stylized facts of financial time series, but performance varies by architecture.
Study compares forecasting methods for logistics time series.
problem Improving forecasting accuracy in logistics.
method Compared statistical and machine learning methods on simulated time series.
result Statistical methods outperformed machine learning in one-step forecasts.
New framework assesses and benchmarks ML methods for multivariate time series.
problem Benchmarking and explaining performance of machine learning methods.
method Proposes a new framework with systematized performance-explainability characteristics.
result Illustrates application to multivariate time series classifiers.
Deep learning detects novel changes in time series data.
problem Detecting novel changes in time series with unknown probability structures.
method Causally extracts an innovations sequence for novelty detection.
result Minimax optimality established for the novelty detection method.
OML-AD detects anomalies in non-stationary time series data.
problem Anomaly detection in non-stationary time series data.
method Online machine learning for anomaly detection.
result OML-AD outperforms state-of-the-art methods in accuracy and efficiency.
A model combines GRU-D for missing data and Neural ODEs for time series continuity.
problem Challenges of informative missingness in multivariate time series data.
method Combines GRU-D for missing data imputation and Neural ODEs for temporal continuity.
result Demonstrates improved performance on a time series classification task.
Study improves GFM accuracy with time series augmentation.
problem Limited time series data hinders GFM performance.
method Data augmentation techniques (GRATIS, MBB, DBA) and transfer learning.
result Significant improvement in GFM accuracy over baseline.
A new unsupervised contrastive learning framework improves time series representation learning.
problem Lack of labeled data in time series data.
method Proposes an unsupervised contrastive learning framework using a novel contrastive loss and data augmentation.
result Framework outperforms other approaches on univariate and multivariate time series, and benefits transfer learning.
HHT feature generation enhances financial time series forecasting.
problem Forecasting nonstationary financial time series.
method CEEMD and HHT for decomposition, machine learning integration.
result HHT-enhanced models outperform traditional models in forecasting.
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.
Time Series Classification (TSC) has been an important and challenging task in data mining, especially on multivariate time series and multi-view time series data sets. Meanwhile, transfer learning has been widely applied in computer vision and natural language processing applications to improve deep neural network's g…
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.