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 1851–1875 of 6748 papers

TitleStatusHype
A General Description of Growth Trends—0
Imbedding Deep Neural NetworksCode0
Deep Learning MacroeconomicsCode0
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer—0
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series ForecastingCode2
N-HiTS: Neural Hierarchical Interpolation for Time Series ForecastingCode2
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV FeedbackCode0
Unifying Pairwise Interactions in Complex DynamicsCode2
Time-Series Anomaly Detection with Implicit Neural RepresentationCode1
Dynamic Temporal Reconciliation by Reinforcement learning—0
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
Unsupervised Change Detection using DRE-CUSUM—0
Robust Augmentation for Multivariate Time Series Classification—0
Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models—0
Learning Mixtures of Linear Dynamical Systems—0
S^3NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks—0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
Differentially-Private Heat and Electricity Markets Coordination—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
Neural Information Squeezer for Causal EmergenceCode1
Multiscaling and rough volatility: an empirical investigation—0
Estimating and backtesting risk under heavy tails—0
Regime recovery using implied volatility in Markov modulated market modelCode0
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series—0
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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