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 2351–2375 of 6748 papers

TitleStatusHype
A novel spectral method for inferring general diploid selection from time series genetic data—0
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs—0
A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems—0
A hybrid neuro--wavelet predictor for QoS control and stability—0
Explaining Outcomes of Multi-Party Dialogues using Causal Learning—0
CDPS: Constrained DTW-Preserving Shapelets—0
Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing—0
A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media—0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis—0
Entanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting—0
Adaptive deep learning for nonlinear time series models—0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Explaining Time Series by Counterfactuals—0
Explicit-Duration Markov Switching Models—0
Exploiting Topic based Twitter Sentiment for Stock Prediction—0
Error Autocorrelation Objective Function for Improved System Modeling—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
Entropy Analysis of Financial Time Series—0
Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian Crisis—0
Explainable Framework for Time-series Analysis via Topological Data Analysis—0
Entropy-based Discovery of Summary Causal Graphs in Time Series—0
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting—0
Epidemic parameters for COVID-19 in several regions of India—0
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations—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