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 3226–3250 of 6748 papers

TitleStatusHype
Improving Optimization for Models With Continuous Symmetry Breaking—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Forecast with Forecasts: Diversity Matters—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary—0
Complex systems: features, similarity and connectivity—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility—0
Improving the quality control of seismic data through active learning—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—0
Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series—0
Improving Time Series Classification Algorithms Using Octave-Convolutional Layers—0
Complex systems approach to natural language—0
Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions—0
A Review on Deep Learning in UAV Remote Sensing—0
Complex market dynamics in the light of random matrix theory—0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
Complexity Measures and Features for Times Series classification—0
A Review of Wind Speed and Wind Power Forecasting Techniques—0
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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