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 4976–5000 of 6748 papers

TitleStatusHype
Super-resolution of Time-series Labels for Bootstrapped Event Detection—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Patch LearningCode0
Learning low-dimensional state embeddings and metastable clusters from time series data—0
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate—0
Multimodal Transformer for Unaligned Multimodal Language SequencesCode0
Using Clinical Notes for ICU Management—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
On Policy Evaluation with Aggregate Time-Series Shocks—0
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends—0
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series—0
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in DrosophilaCode0
Efficient Covariance Estimation from Temporal DataCode0
Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018—0
Monotonic Gaussian Process FlowCode0
Learning the Non-linearity in Convolutional Neural Networks—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Deep Factors for Forecasting—0
Graph-based era segmentation of international financial integration—0
Evaluating time series forecasting models: An empirical study on performance estimation methodsCode0
BreizhCrops: A Time Series Dataset for Crop Type MappingCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
ODE^2VAE: Deep generative second order ODEs with Bayesian neural networksCode0
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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