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 3376–3400 of 6748 papers

TitleStatusHype
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces—0
Sequence to sequence deep learning models for solar irradiation forecasting—0
Sequential asset ranking in nonstationary time series—0
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions—0
Sequential convolutional network for behavioral pattern extraction in gait recognition—0
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains—0
Sequential Monte Carlo With Model Tempering—0
A Joint Model for IT Operation Series Prediction and Anomaly Detection—0
Sequential visibility-graph motifs—0
Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization—0
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
SeriesNet:A Generative Time Series Forecasting Model—0
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting—0
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons—0
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference—0
Shapelets for earthquake detection—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Sharing Features among Dynamical Systems with Beta Processes—0
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models—0
Shortening Time Required for Adaptive Structural Learning Method of Deep Belief Network with Multi-Modal Data Arrangement—0
Use Short Isometric Shapelets to Accelerate Binary Time Series Classification—0
Short note on the behavior of recurrent neural network for noisy dynamical system—0
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data—0
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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