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 3576–3600 of 6748 papers

TitleStatusHype
Technical Progress Analysis Using a Dynamic Topic Model for Technical Terms to Revise Patent Classification Codes—0
Using Spatio-temporal Deep Learning for Forecasting Demand and Supply-demand Gap in Ride-hailing System with Anonymised Spatial Adjacency Information—0
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting—0
United States FDA drug approvals are persistent and polycyclic: Insights into economic cycles, innovation dynamics, and national policy—0
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management—0
Anomaly Detection and Localization based on Double Kernelized Scoring and Matrix Kernels—0
Detection of Anomalies in a Time Series Data using InfluxDB and Python—0
Proofs and additional experiments on Second order techniques for learning time-series with structural breaks—0
Clustering high dimensional meteorological scenarios: results and performance index—0
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies—0
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODECode0
Building Deep Learning Models to Predict Mortality in ICU Patients—0
Interpolation and Gap Filling of Landsat Reflectance Time Series—0
A Review of Hidden Markov Models and Recurrent Neural Networks for Event Detection and Localization in Biomedical Signals—0
Quantifying Synchronization in a Biologically Inspired Neural Network—0
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics—0
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes—0
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation—0
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis—0
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data—0
Estimation of Large Financial Covariances: A Cross-Validation Approach—0
Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers—0
Parametric measures of variability induced by risk measures—0
Enhanced Recurrent Neural Tangent Kernels for Non-Time-Series DataCode0
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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