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 5276–5300 of 6748 papers

TitleStatusHype
Closed-form Inference and Prediction in Gaussian Process State-Space Models—0
Applying Nature-Inspired Optimization Algorithms for Selecting Important Timestamps to Reduce Time Series Dimensionality—0
A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention—0
Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction—0
Time Series Featurization via Topological Data Analysis—0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps—0
Anomaly detection with Wasserstein GAN—0
A novel health risk model based on intraday physical activity time series collected by smartphones—0
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time SeriesCode0
Estimation of multivariate asymmetric power GARCH models—0
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data—0
Modeling Irregularly Sampled Clinical Time SeriesCode0
Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series—0
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability—0
Examining Deep Learning Architectures for Crime Classification and Prediction—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
Large Spectral Density Matrix Estimation by Thresholding—0
Imputation of Clinical Covariates in Time Series—0
Improving Clinical Predictions through Unsupervised Time Series Representation Learning—0
Learning filter widths of spectral decompositions with waveletsCode0
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices—0
Extracting Relationships by Multi-Domain MatchingCode0
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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