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

Showing 801850 of 6748 papers

TitleStatusHype
HARNet: A Convolutional Neural Network for Realized Volatility ForecastingCode1
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load ForecastingCode1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization ApproachCode1
SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time SeriesCode1
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
Sig-Wasserstein GANs for Time Series GenerationCode1
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting EpidemicsCode1
Simulation platform for pattern recognition based on reservoir computing with memristor networksCode1
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature ExtractionCode1
A Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsCode1
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural NetworksCode1
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint DetectionCode1
Ensembles of Localised Models for Time Series ForecastingCode1
Convolution-enhanced Evolving Attention NetworksCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Solving high-dimensional parameter inference: marginal posterior densities & Moment NetworksCode1
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
High-Dimensional Granger Causality Tests with an Application to VIX and NewsCode1
Spacecraft Anomaly Detection with Attention Temporal Convolution NetworkCode1
Heteroscedastic Temporal Variational Autoencoder For Irregular Time SeriesCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecastingCode1
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic ForecastingCode1
Spatio-Temporal Graph Convolution for Resting-State fMRI AnalysisCode1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly BenchmarkCode1
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional StrategiesCode1
Fast Variational Learning in State-Space Gaussian Process ModelsCode1
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC AlgorithmCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial NetworkCode1
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative NetworkCode1
Explainable Deep Convolutional Candlestick LearnerCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Expressing Multivariate Time Series as Graphs with Time Series Attention TransformerCode1
Explaining Time Series Predictions with Dynamic MasksCode1
Stop&Hop: Early Classification of Irregular Time SeriesCode1
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series ClassificationCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
Fast, Accurate and Interpretable Time Series Classification Through RandomizationCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
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Benchmark Results

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