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

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Showing 651–675 of 6748 papers

TitleStatusHype
Conformal prediction set for time-seriesCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
Conformal Time-series ForecastingCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series DataCode1
Active multi-fidelity Bayesian online changepoint detectionCode1
Continuous Latent Process FlowsCode1
Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and ApplicationCode1
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time SeriesCode1
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
Continuous-Time Deep Glioma Growth ModelsCode1
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-SeriesCode1
Contrastive Learning for Unsupervised Domain Adaptation of Time SeriesCode1
Arbitrage-free neural-SDE market modelsCode1
Convolutional Radio Modulation Recognition NetworksCode1
Convolution-enhanced Evolving Attention NetworksCode1
Random Dilated Shapelet Transform: A New Approach for Time Series ShapeletsCode1
FastDTW is approximate and Generally Slower than the Algorithm it ApproximatesCode1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
Few-Shot Forecasting of Time-Series with Heterogeneous ChannelsCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
Explaining Time Series Predictions with Dynamic MasksCode1
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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