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 6576–6600 of 6748 papers

TitleStatusHype
Dimensionality reduction for time series data—0
A hybrid neuro--wavelet predictor for QoS control and stability—0
Variational inference of latent state sequences using Recurrent Networks—0
Supervised classification-based stock prediction and portfolio optimization—0
Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data—0
Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts—0
Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems—0
Multi-adaptive Natural Language Generation using Principal Component Regression—0
Unsupervised Event Coreference Resolution—0
An Ordered Lasso and Sparse Time-Lagged Regression—0
Effective Bayesian Modeling of Groups of Related Count Time Series—0
Training Deep Fourier Neural Networks To Fit Time-Series Data—0
A consistent deterministic regression tree for non-parametric prediction of time series—0
Spatial Neural Networks and their Functional Samples: Similarities and Differences—0
\#mygoal: Finding Motivations on Twitter—0
Benchmarking Twitter Sentiment Analysis Tools—0
Implementing spectral methods for hidden Markov models with real-valued emissionsCode0
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions—0
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning—0
Ensemble Committees for Stock Return Classification and Prediction—0
Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach—0
Finding middle ground? Multi-objective Natural Language Generation from time-series data—0
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach—0
Spectral Correlation Hub Screening of Multivariate Time Series—0
Sleep Analytics and Online Selective Anomaly Detection—0
Show:102550
← PrevPage 264 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