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 5451–5475 of 6748 papers

TitleStatusHype
Testing of Binary Regime Switching Models using Squeeze Duration Analysis—0
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing—0
SeriesNet:A Generative Time Series Forecasting Model—0
Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale—0
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks—0
Data Consistency Approach to Model ValidationCode0
Deep Learning for Energy Markets—0
LARNN: Linear Attention Recurrent Neural NetworkCode0
Short-term load forecasting using optimized LSTM networks based on EMD—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction—0
Study of Set-Membership Adaptive Kernel Algorithms—0
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values—0
Data augmentation using synthetic data for time series classification with deep residual networksCode0
OptStream: Releasing Time Series Privately—0
Predicting Learning Status in MOOCs using LSTM—0
Real-time Change Point Detection using On-line Topic Models—0
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks—0
Mod-DeepESN: Modular Deep Echo State Network—0
Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model—0
Modeling joint probability distribution of yield curve parameters—0
Kernel Density Estimation-Based Markov Models with Hidden State—0
On the use of Singular Spectrum Analysis—0
Discovering physical concepts with neural networksCode0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—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