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 2651–2675 of 6748 papers

TitleStatusHype
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable StocksCode1
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
pyWATTS: Python Workflow Automation Tool for Time SeriesCode1
On the benefits of maximum likelihood estimation for Regression and Forecasting—0
Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit—0
Minimax Estimation of Partially-Observed Vector AutoRegressionsCode0
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification—0
Voice2Series: Reprogramming Acoustic Models for Time Series ClassificationCode1
SCINet: Time Series Modeling and Forecasting with Sample Convolution and InteractionCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
Adaptive Visibility Graph Neural Network and It's Application in Modulation Classification—0
Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL—0
Adversarial Attacks on Deep Models for Financial Transaction RecordsCode0
Trend-Following Strategies via Dynamic Momentum Learning—0
Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks—0
Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)—0
A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread—0
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning—0
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian FilteringCode1
The Economic Impact of Critical National Infrastructure Failure Due to Space Weather—0
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification—0
Next Generation Reservoir ComputingCode1
A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface—0
Signal processing on simplicial complexes—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