SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 451–500 of 6748 papers

TitleStatusHype
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
Forecasting with Deep LearningCode1
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Graphical estimation of multivariate count time series—0
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events—0
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech EnhancementCode1
Temporal Graph Neural Networks for Irregular DataCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
Excess risk bound for deep learning under weak dependence—0
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?—0
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand—0
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes—0
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
Continuous-time convolutions model of event sequencesCode0
One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular DataCode1
Fourier-RNNs for Modelling Noisy Physics Data—0
Label-efficient Time Series Representation Learning: A Review—0
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Estimating Driver Personality Traits from On-Road Driving Data—0
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel MixingCode2
Weakly Supervised Anomaly Detection: A SurveyCode1
Dynamic and Stochastic Rational Behavior—0
High-Dimensional Granger Causality for Climatic Attribution—0
DeepVATS: Deep Visual Analytics for Time SeriesCode1
Short-Term Memory Convolutions—0
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining ApproachCode1
Unsupervised Deep Learning for IoT Time Series—0
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series DataCode0
Market-Based Probability of Stock Returns—0
Noise-cleaning the precision matrix of fMRI time series—0
Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification—0
Identifiability of latent-variable and structural-equation models: from linear to nonlinear—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified