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 3326–3350 of 6748 papers

TitleStatusHype
On the LRD of the Aggregated Traffic Flows in High-Speed Computer Networks—0
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise—0
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning—0
Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models—0
Exploring the representativeness of the M5 competition data—0
Modeling Macroeconomic Variations After COVID-19—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment—0
Two-Stage Framework for Seasonal Time Series Forecasting—0
Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention—0
Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?Code0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference—0
Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series—0
A Spectral Enabled GAN for Time Series Data Generation—0
Variance Reduced Training with Stratified Sampling for Forecasting Models—0
A Novel CNN-LSTM-based Approach to Predict Urban Expansion—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Dynamic and Thermodynamic Models of Adaptation—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
Automated data-driven approach for gap filling in the time series using evolutionary learningCode0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
The Kernel Trick for Nonlinear Factor Modeling—0
CLPVG: Circular limited penetrable visibility graph as a new network model for time series—0
Unsupervised dynamic modeling of medical image transformationCode0
Show:102550
← PrevPage 134 of 270Next →
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