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 351–375 of 6748 papers

TitleStatusHype
Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport—0
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting—0
Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning—0
Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns—0
Model scale versus domain knowledge in statistical forecasting of chaotic systemsCode2
Mobile Mapping Mesh Change Detection and Update—0
Spacecraft Anomaly Detection with Attention Temporal Convolution NetworkCode1
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study—0
Hybrid Variational Autoencoder for Time Series Forecasting—0
A Fourier Transform Approach for Automatic Detection of Oysters Spawning—0
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena—0
Accurate Prediction of Global Mean Temperature through Data Transformation Techniques—0
Explainable AI for Time Series via Virtual Inspection Layers—0
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM SignalsCode0
A Novel Method Combines Moving Fronts, Data Decomposition and Deep Learning to Forecast Intricate Time Series—0
Machine Learning Enhanced Hankel Dynamic-Mode Decomposition—0
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG SignalsCode0
Local-Global Methods for Generalised Solar Irradiance Forecasting—0
TSMixer: An All-MLP Architecture for Time Series ForecastingCode1
Multi-task Meta Label Correction for Time Series PredictionCode0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence—0
Learning Representation for Anomaly Detection of Vehicle Trajectories—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