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 1651–1675 of 6748 papers

TitleStatusHype
Two methods for Jamming Identification in UAVs Networks using New Synthetic Dataset—0
Generative Adversarial Network for Future Hand Segmentation from Egocentric VideoCode0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series—0
Prediction Algorithm for Heat Demand of Science and Technology Topics Based on Time Convolution Network—0
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices—0
Learning latent causal relationships in multiple time series—0
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix FactorizationCode1
Learning Whole Heart Mesh Generation From Patient Images For Computational SimulationsCode1
Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series—0
Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space—0
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies—0
Selection of entropy based features for the analysis of the Archimedes' spiral applied to essential tremor—0
WOODS: Benchmarks for Out-of-Distribution Generalization in Time SeriesCode1
SepTr: Separable Transformer for Audio Spectrogram ProcessingCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
Generalized Classification of Satellite Image Time Series with Thermal Positional EncodingCode1
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study—0
Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics—0
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time SeriesCode1
Transfer learning for cross-modal demand prediction of bike-share and public transit—0
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model—0
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series DataCode2
DEPTS: Deep Expansion Learning for Periodic Time Series ForecastingCode1
What is the best RNN-cell structure for forecasting each time series behavior?—0
Joint Time-Vertex Fractional Fourier Transform—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