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 176200 of 6748 papers

TitleStatusHype
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series ForecastingCode1
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather StationsCode1
MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical NotesCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional StrategiesCode1
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Forecasting with Deep LearningCode1
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
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular DataCode1
Weakly Supervised Anomaly Detection: A SurveyCode1
DeepVATS: Deep Visual Analytics for Time SeriesCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining ApproachCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
Window Size Selection in Unsupervised Time Series Analytics: A Review and BenchmarkCode1
SimMTM: A Simple Pre-Training Framework for Masked Time-Series ModelingCode1
Recurrences reveal shared causal drivers of complex 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