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 1276–1300 of 6748 papers

TitleStatusHype
Continual Learning Using Bayesian Neural Networks—0
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data—0
Analysis of EEG data using complex geometric structurization—0
A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters—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
Asymptotic nonparametric statistical analysis of stationary time series—0
Analysis of cyclical behavior in time series of stock market returns—0
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring—0
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces—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 comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers—0
A Bayesian Approach to Sparse plus Low rank Network Identification—0
Asymmetric Distributions from Constrained Mixtures—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
A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas—0
A Survey on Time-Series Distance Measures—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
A Deep Learning Forecaster with Exogenous Variables for Day-Ahead Locational Marginal Price—0
A Comparative Analysis of Machine Learning and Grey Models—0
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series—0
Analysis of Blink Rate Variability during reading and memory testing—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