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 2551–2575 of 6748 papers

TitleStatusHype
Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning—0
On the short term stability of financial ARCH price processes—0
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution TestsCode1
Multiplicative Error Models: 20 years on—0
Deep Autoregressive Models with Spectral AttentionCode1
Wasserstein GAN: Deep Generation applied on Bitcoins financial time series—0
Fast-Slow Streamflow Model Using Mass-Conserving LSTM—0
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting—0
Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training—0
Sliding Spectrum Decomposition for Diversified Recommendation—0
Learning interaction rules from multi-animal trajectories via augmented behavioral modelsCode0
Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and ResultsCode1
STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership PredictionCode0
Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets—0
A deep convolutional neural network that is invariant to time rescalingCode0
Scaled-Time-Attention Robust Edge Network—0
Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976 2020) Portends Intrinsic Industrial Transformation and Independence of Certain Crises—0
Physics-informed generative neural network: an application to troposphere temperature prediction—0
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting—0
Deep Metric Learning Model for Imbalanced Fault Diagnosis—0
Probabilistic Time Series Forecasting with Implicit Quantile NetworksCode2
Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based EnsemblesCode1
Numerical approximation of hybrid Poisson-jump Ait-Sahalia-type interest rate model with delay—0
Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks—0
Generation of Synthetic Multi-Resolution Time Series Load Data—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