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 1276–1300 of 6748 papers

TitleStatusHype
Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation—0
K-ARMA Models for Clustering Time Series Data—0
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—0
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
Continual Learning for Human State MonitoringCode0
Imaging the time series of one single referenced EEG electrode for Epileptic Seizures Risk Analysis—0
An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows—0
Hidden Parameter Recurrent State Space Models For Changing Dynamics ScenariosCode0
Intrinsic Anomaly Detection for Multi-Variate Time Series—0
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and EmbeddingCode0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—0
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series ForecastingCode1
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEsCode1
Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors—0
Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition—0
Generative Anomaly Detection for Time Series Datasets—0
TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data AugmentationCode1
Learning Deep Input-Output Stable DynamicsCode0
A Strategy Optimized Pix2pix Approach for SAR-to-Optical Image Translation Task—0
Local Evaluation of Time Series Anomaly Detection AlgorithmsCode1
AI for trading strategies—0
fETSmcs: Feature-based ETS model component selectionCode0
Data Augmentation techniques in time series domain: A survey and taxonomy—0
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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