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 326–350 of 6748 papers

TitleStatusHype
Contrastive Neural Processes for Self-Supervised LearningCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
COVID-19 Data Analysis and Forecasting: Algeria and the WorldCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Data-driven discovery of intrinsic dynamicsCode1
Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin PlateCode1
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time WarpingCode1
Conformal prediction set for time-seriesCode1
Can LLMs Understand Time Series Anomalies?Code1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Deep ConvLSTM with self-attention for human activity decoding using wearablesCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Deep Isolation Forest for Anomaly DetectionCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
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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