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 726–750 of 6748 papers

TitleStatusHype
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models—0
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback—0
Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios—0
Understanding Cryptocoins Trends CorrelationsCode1
Investigation of Proper Orthogonal Decomposition for Echo State Networks—0
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data—0
Predicting China's CPI by Scanner Big Data—0
Context-Aware Ensemble Learning for Time Series—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
Score-based calibration testing for multivariate forecast distributionsCode0
Triadic Temporal Exponential Random Graph Models (TTERGM)—0
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation—0
Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty—0
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring—0
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting—0
Beyond S-curves: Recurrent Neural Networks for Technology Forecasting—0
SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image UnderstandingCode2
PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting—0
Federated Learning for 5G Base Station Traffic ForecastingCode1
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
Evaluation of Entropy and Fractal Dimension as Biomarkers for Tumor Growth and Treatment Response using Cellular Automata—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
Spatio-Temporal Meta-Graph Learning for Traffic ForecastingCode1
A Time Series is Worth 64 Words: Long-term Forecasting with TransformersCode5
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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