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 951–975 of 6748 papers

TitleStatusHype
Unsupervised Model Selection for Time-series Anomaly DetectionCode1
Review of Clustering Methods for Functional Data—0
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features—0
Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic PredictionCode0
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series ClassifiersCode0
Grouped self-attention mechanism for a memory-efficient Transformer—0
Finding Scientific Topics in Continuously Growing Text CorporaCode1
Solar Power Time Series Forecasting Utilising Wavelet Coefficients—0
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised LearningCode1
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring—0
Fast Inference for Quantile Regression with Tens of Millions of Observations—0
A case study of spatiotemporal forecasting techniques for weather forecastingCode0
Explainable classification of astronomical uncertain time series—0
Masked Multi-Step Multivariate Time Series Forecasting with Future Information—0
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets—0
Non-contrastive representation learning for intervals from well logs—0
Towards Automatic Forecasting: Evaluation of Time-Series Forecasting Models for Chickenpox Cases Estimation in Hungary—0
Experimental study of time series forecasting methods for groundwater level predictionCode0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Retrieval Based Time Series Forecasting—0
Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values—0
Neural parameter calibration for large-scale multi-agent modelsCode1
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness—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