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 901–950 of 6748 papers

TitleStatusHype
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series ClassificationCode1
Generative ODE Modeling with Known UnknownsCode1
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time SeriesCode1
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
A Critical Review of Recurrent Neural Networks for Sequence LearningCode0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological ForecastingCode0
Interpolation-Prediction Networks for Irregularly Sampled Time SeriesCode0
Integrating Attention Feedback into the Recurrent Neural NetworkCode0
Probabilistic AutoRegressive Neural Networks for Accurate Long-range ForecastingCode0
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time seriesCode0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality DiscoveryCode0
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural NetworksCode0
Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer InterfacesCode0
Inferring species interactions using Granger causality and convergent cross mappingCode0
An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in RCode0
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
An Information Theory Approach on Deciding Spectroscopic Follow UpsCode0
Inferring network connectivity from event timing patternsCode0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
imputeTS: Time Series Missing Value Imputation in RCode0
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Improving COVID-19 Forecasting using eXogenous VariablesCode0
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided EmbeddingCode0
An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar ObservatoryCode0
A Better Alternative to Piecewise Linear Time Series SegmentationCode0
Improving Accuracy and Explainability of Online Handwriting RecognitionCode0
Gradient Importance Learning for Incomplete ObservationsCode0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GANCode0
Imbedding Deep Neural NetworksCode0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returnsCode0
Identifying cross country skiing techniques using power meters in ski polesCode0
AverageTime: Enhance Long-Term Time Series Forecasting with Simple AveragingCode0
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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