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 226–250 of 6748 papers

TitleStatusHype
Deep Isolation Forest for Anomaly DetectionCode1
Data-driven discovery of intrinsic dynamicsCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
ClaSP - Time Series SegmentationCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Deep Learning Statistical ArbitrageCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Adversarial Sparse Transformer for Time Series ForecastingCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
DeepSITH: Efficient Learning via Decomposition of What and When Across Time ScalesCode1
Deep Stock PredictionsCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
DEPTS: Deep Expansion Learning for Periodic Time Series ForecastingCode1
A bio-inspired bistable recurrent cell allows for long-lasting memoryCode1
Detection of gravitational-wave signals from binary neutron star mergers using machine learningCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
A biologically plausible neural network for Slow Feature AnalysisCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
Diffusion models for missing value imputation in tabular dataCode1
Changing Fashion CulturesCode1
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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