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 3776–3800 of 6748 papers

TitleStatusHype
Score-informed Networks for Music Performance AssessmentCode1
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and their InteractionsCode0
An Empirical Survey of Data Augmentation for Time Series Classification with Neural NetworksCode1
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising ModelCode0
Random Forests for dependent dataCode1
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models—0
Rethinking Recurrent Neural Networks and Other Improvements for Image ClassificationCode1
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic ForecastingCode1
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
Structural Inference in Sparse High-Dimensional Vector Autoregressions—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
Calibration of Google Trends Time SeriesCode1
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
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China—0
Multi-stream RNN for Merchant Transaction Prediction—0
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using TwitterCode0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection—0
Graph Gamma Process Generalized Linear Dynamical SystemsCode0
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
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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