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 2151–2175 of 6748 papers

TitleStatusHype
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systemsCode1
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models—0
Sig-Wasserstein GANs for Time Series GenerationCode1
Brain dynamics via Cumulative Auto-Regressive Self-Attention—0
Nested Multiple Instance Learning with Attention MechanismsCode0
ECG synthesis with Neural ODE and GAN models—0
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE—0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Robust and efficient change point detection using novel multivariate rank-energy GoF test—0
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index—0
Improved FRQI on superconducting processors and its restrictions in the NISQ era—0
Aligned Multi-Task Gaussian Process—0
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic TomographyCode0
Multi-Task Neural ProcessesCode0
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Coresets for Time Series Clustering—0
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction ModelsCode0
Warped Dynamic Linear Models for Time Series of CountsCode0
Testing and Estimating Structural Breaks in Time Series and Panel Data in StataCode1
GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series—0
Forecasting with a Panel Tobit Model—0
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series 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