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 3976–4000 of 6748 papers

TitleStatusHype
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks—0
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their InteractionsCode0
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising ModelCode0
Structural Inference in Sparse High-Dimensional Vector Autoregressions—0
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing—0
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard AssessmentCode0
Perpetual Motion: Generating Unbounded Human Motion—0
Water Quality Prediction on a Sigfox-compliant IoT Device: The Road Ahead of WaterS—0
Regularized Flexible Activation Function Combinations for Deep Neural Networks—0
Benchmarking Multivariate Time Series Classification Algorithms—0
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning—0
Bid Prediction in Repeated Auctions with Learning—0
Graph Gamma Process Generalized Linear Dynamical SystemsCode0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection—0
Multi-stream RNN for Merchant Transaction Prediction—0
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China—0
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using TwitterCode0
Multinomial Sampling for Hierarchical Change-Point Detection—0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News—0
Hide-and-Seek Privacy ChallengeCode0
bootUR: An R Package for Bootstrap Unit Root Tests—0
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks—0
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children—0
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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