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 3351–3375 of 6748 papers

TitleStatusHype
Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study—0
Patterns of Urban Foot Traffic Dynamics—0
PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction—0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
PECAIQR: A Model for Infectious Disease Applied to the Covid-19 Epidemic—0
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
Perceiving the arrow of time in autoregressive motion—0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion—0
Performance Dependency of LSTM and NAR Beamformers With Respect to Sensor Array Properties in V2I Scenario—0
Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series—0
Periodic Freight Demand Estimation for Large-scale Tactical Planning—0
Permutation-based tests for discontinuities in event studies—0
Perpetual Motion: Generating Unbounded Human Motion—0
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features—0
Persistence-Based Discretization for Learning Discrete Event Systems from Time Series—0
Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting—0
Persistent Homology of Attractors For Action Recognition—0
Persistent Homology of Coarse Grained State Space Networks—0
Personality-Driven Gaze Animation with Conditional Generative Adversarial Networks—0
Personalized Online Machine Learning—0
Personalized Pose Forecasting—0
Person Re-Identification by Unsupervised Video Matching—0
Pets: General Pattern Assisted Architecture For Time Series Analysis—0
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting—0
Show:102550
← PrevPage 135 of 270Next →
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