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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 6748 papers

TitleStatusHype
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
On the Benefits of Biophysical Synapses—0
Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model—0
Multimodal Multi-User Surface Recognition with the Kernel Two-Sample TestCode0
Robust Multimodal Fusion for Human Activity Recognition—0
Vector Quantized Time Series Generation with a Bidirectional Prior ModelCode1
A Kalman Filter Framework for Resolving 3D Displacement Field Time Series By Combining Multitrack Multitemporal InSAR and GNSS Horizontal Velocities—0
Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with LoopsCode0
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT—0
Robust Dominant Periodicity Detection for Time Series with Missing Data—0
Time series anomaly detection with reconstruction-based state-space modelsCode0
eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)—0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders—0
Synthetic ECG Signal Generation using Probabilistic Diffusion ModelsCode1
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent SpaceCode0
Spectral learning of Bernoulli linear dynamical systems modelsCode0
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming—0
Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept—0
Building Floorspace in China: A Dataset and Learning Pipeline—0
Anamnesic Neural Differential Equations with Orthogonal Polynomial ProjectionsCode0
Interpretable System Identification and Long-term Prediction on Time-Series Data—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked AutoencodersCode1
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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