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 1926–1950 of 6748 papers

TitleStatusHype
K-ARMA Models for Clustering Time Series Data—0
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes—0
Intrinsic Anomaly Detection for Multi-Variate Time Series—0
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
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
Continual Learning for Human State MonitoringCode0
Hidden Parameter Recurrent State Space Models For Changing Dynamics ScenariosCode0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—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
Generative Anomaly Detection for Time Series Datasets—0
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
Learning Deep Input-Output Stable DynamicsCode0
A Strategy Optimized Pix2pix Approach for SAR-to-Optical Image Translation Task—0
fETSmcs: Feature-based ETS model component selectionCode0
AI for trading strategies—0
Data Augmentation techniques in time series domain: A survey and taxonomy—0
ML-Based Approach for NFL Defensive Pass Interference Prediction Using GPS Tracking DataCode0
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting—0
Undetectable GPS-Spoofing Attack on Time Series Phasor Measurement Unit Data—0
Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural NetworksCode0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—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