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 3851–3900 of 6748 papers

TitleStatusHype
Predictive maintenance on event logs: Application on an ATM fleet—0
Time series classification for varying length series—0
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior—0
Time-Series Classification Through Histograms of Symbolic Polynomials—0
Time Series Classification to Improve Poultry Welfare—0
Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets—0
Time Series Classification using the Hidden-Unit Logistic Model—0
Time Series Classification via Topological Data Analysis—0
Time series clustering based on the characterisation of segment typologies—0
Time Series Clustering for Grouping Products Based on Price and Sales Patterns—0
Time Series Clustering for Human Behavior Pattern Mining—0
Time series cluster kernels to exploit informative missingness and incomplete label information—0
Time Series Comparisons in Deep Space Network—0
Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder—0
Time Series Counterfactual Inference with Hidden Confounders—0
Time Series Data Augmentation for Deep Learning: A Survey—0
Time Series Data Imputation: A Survey on Deep Learning Approaches—0
Time Series Data Mining Algorithms Towards Scalable and Real-Time Behavior Monitoring—0
Time Series Deinterleaving of DNS Traffic—0
Time Series Domain Adaptation via Sparse Associative Structure Alignment—0
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance—0
Time-Series Estimation from Randomly Time-Warped Observations—0
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data—0
Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets—0
Time Series Featurization via Topological Data Analysis—0
A novel weighted approach for time series forecasting based on visibility graph—0
Time Series Forecasting Models Copy the Past: How to Mitigate—0
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods—0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Time Series Forecasting (TSF) Using Various Deep Learning Models—0
Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey—0
Time Series Forecasting Using Manifold Learning—0
Time series forecasting using neural networks—0
Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window—0
Time Series Forecasting With Deep Learning: A Survey—0
Time Series Forecasting with Hypernetworks Generating Parameters in Advance—0
Time Series Forecasting with Stacked Long Short-Term Memory Networks—0
Time-series Generative Adversarial Networks—0
Time-series Generative Adversarial Networks—0
Time-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis—0
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies—0
Time Series Imputation—0
Time-series Imputation of Temporally-occluded Multiagent Trajectories—0
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff—0
Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States—0
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs—0
Time Series Language Model for Descriptive Caption Generation—0
Time Series Learning using Monotonic Logical Properties—0
Time Series Model Attribution Visualizations as Explanations—0
Time series modeling by a regression approach based on a latent process—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