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 801–825 of 6748 papers

TitleStatusHype
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic ForecastingCode1
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)Code1
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov ModelCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
FastDTW is approximate and Generally Slower than the Algorithm it ApproximatesCode1
Generative ODE Modeling with Known UnknownsCode1
Spatio-Temporal Graph Convolution for Resting-State fMRI AnalysisCode1
Human Activity Recognition from Wearable Sensor Data Using Self-AttentionCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
An Evaluation of Change Point Detection AlgorithmsCode1
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure EvolutionCode1
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural NetworksCode1
Adversarial Attacks on Probabilistic Autoregressive Forecasting ModelsCode1
On the performance of deep learning models for time series classification in streamingCode1
Forecasting Sequential Data using Consistent Koopman AutoencodersCode1
Dimensionality reduction to maximize prediction generalization capabilityCode1
A Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsCode1
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classificationCode1
Modeling Continuous Stochastic Processes with Dynamic Normalizing FlowsCode1
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity DetectionCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural NetworkCode1
Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning TechniquesCode1
Deep reconstruction of strange attractors from time seriesCode1
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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