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 5601–5625 of 6748 papers

TitleStatusHype
Nonlinear Deconvolution by Sampling Biophysically Plausible Hemodynamic Models—0
An Incremental Boolean Tensor Factorization approach to model Change Patterns of Objects in Images—0
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligenceCode0
Chatter Classification in Turning Using Machine Learning and Topological Data Analysis—0
Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk ModelingCode1
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks—0
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples—0
Seglearn: A Python Package for Learning Sequences and Time SeriesCode0
Reservoir computing approaches for representation and classification of multivariate time seriesCode0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
Universal features of price formation in financial markets: perspectives from Deep Learning—0
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process—0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Coordinating users of shared facilities via data-driven predictive assistants and game theory—0
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM—0
Theory and Algorithms for Forecasting Time Series—0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology—0
Adaptive Kernel Estimation of the Spectral Density with Boundary Kernel Analysis—0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
Sales forecasting using WaveNet within the framework of the Kaggle competition—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
Construction of neural networks for realization of localized deep learning—0
DeepMoTIon: Learning to Navigate Like Humans—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