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 901–925 of 6748 papers

TitleStatusHype
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio EstimationCode1
Transfer learning for time series classificationCode1
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Inferring network connectivity from event timing patternsCode0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
Inferring species interactions using Granger causality and convergent cross mappingCode0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
A Critical Review of Recurrent Neural Networks for Sequence LearningCode0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
imputeTS: Time Series Missing Value Imputation in RCode0
Gradient Importance Learning for Incomplete ObservationsCode0
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Improving COVID-19 Forecasting using eXogenous VariablesCode0
Probabilistic AutoRegressive Neural Networks for Accurate Long-range ForecastingCode0
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
Improving Accuracy and Explainability of Online Handwriting RecognitionCode0
Implementing spectral methods for hidden Markov models with real-valued emissionsCode0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You ThinkCode0
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality DiscoveryCode0
Impact of non-stationarity on estimating and modeling empirical copulas of daily stock returnsCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
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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