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 2601–2625 of 6748 papers

TitleStatusHype
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters—0
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package—0
Asymptotic Theory for Unit Root Moderate Deviations in Quantile Autoregressions and Predictive Regressions—0
Active Learning of Driving Scenario Trajectories—0
A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies—0
Cross-Modal Virtual Sensing for Combustion Instability Monitoring—0
Asymptotic nonparametric statistical analysis of stationary time series—0
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data—0
Analysis of cyclical behavior in time series of stock market returns—0
Cross-Modal Data Programming Enables Rapid Medical Machine Learning—0
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—0
Crossmatching variable objects with the Gaia data—0
Cross-Lingual Topic Alignment in Time Series Japanese / Chinese News—0
Asymmetric excitation of left- and right-tail extreme events probed using a Hawkes model: application to financial returns—0
Analysis of complex circadian time series data using wavelets—0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers—0
Cross-Frequency Time Series Meta-Forecasting—0
Asymmetric Distributions from Constrained Mixtures—0
Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation—0
Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis—0
A symbolic information approach to characterize response-related differences in cortical activity during a Go/No-Go task—0
Analysis of chaotic dynamical systems with autoencoders—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