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 4251–4300 of 6748 papers

TitleStatusHype
Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting—0
What is the best RNN-cell structure for forecasting each time series behavior?—0
What Makes An Asset Useful?—0
What went wrong and when? Instance-wise Feature Importance for Time-series Models—0
What went wrong and when?\\ Instance-wise feature importance for time-series black-box models—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming—0
When is Early Classification of Time Series Meaningful?—0
When Ramanujan meets time-frequency analysis in complicated time series analysis—0
When Traffic Flow Prediction Meets Wireless Big Data Analytics—0
Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits—0
Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?—0
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals—0
Wind power ramp prediction algorithm based on wavelet deep belief network—0
Wind speed forecast using random forest learning method—0
Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Winning the ICCV 2019 Learning to Drive Challenge—0
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands—0
Winterization of Texan power system infrastructure is profitable but risky—0
WISDoM: characterizing neurological timeseries with the Wishart distribution—0
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams—0
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index—0
Word Recognition from Continuous Articulatory Movement Time-series Data using Symbolic Representations—0
XAI Methods for Neural Time Series Classification: A Brief Review—0
Yes, DLGM! A novel hierarchical model for hazard classification—0
Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection—0
You May Not Need Order in Time Series Forecasting—0
ZeLiC and ZeChipC: Time Series Interpolation Methods for Lebesgue or Event-based Sampling—0
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks—0
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition—0
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Improving MF-DFA model with applications in precious metals market—0
Improving Neuroevolution Using Island Extinction and Repopulation—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection—0
Improving self-supervised pretraining models for epileptic seizure detection from EEG data—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility—0
Improving the quality control of seismic data through active learning—0
Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—0
Show:102550
← PrevPage 86 of 135Next →
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