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 3301–3325 of 6748 papers

TitleStatusHype
Tomography of time-dependent quantum spin networks with machine learning—0
A machine learning approach to itinerary-level booking prediction in competitive airline markets—0
Online Learning with Radial Basis Function Networks—0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
A novel weighted approach for time series forecasting based on visibility graph—0
Anticipating synchronization with machine learning—0
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events—0
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning—0
Visualising Deep Network's Time-Series Representations—0
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation—0
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Machine Learning Prediction of Time-Varying Rayleigh Channels—0
Streaming Linear System Identification with Reverse Experience Replay—0
Extension of the Lagrange multiplier test for error cross-section independence to large panels with non normal errors—0
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Generating Reliable Process Event Streams and Time Series Data based on Neural NetworksCode0
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks—0
Classification and Feature Transformation with Fuzzy Cognitive MapsCode0
Optimizing Expected Shortfall under an _1 constraint -- an analytic approach—0
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise—0
Deep Hedging, Generative Adversarial Networks, and Beyond—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