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 6101–6125 of 6748 papers

TitleStatusHype
Robust Online Time Series Prediction with Recurrent Neural Networks—0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series—0
Logic-based Clustering and Learning for Time-Series Data—0
Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model—0
Temporal Feature Selection on Networked Time Series—0
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes—0
Learning binary or real-valued time-series via spike-timing dependent plasticity—0
Automatic time-series phenotyping using massive feature extraction—0
Graphical RNN Models—0
Fast Stability Scanning for Future Grid Scenario Analysis—0
A probabilistic graphical model approach in 30 m land cover mapping with multiple data sources—0
A Review of Intelligent Practices for Irrigation Prediction—0
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification—0
Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016—0
Intra-day Activity Better Predicts Chronic Conditions—0
Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework—0
Positive blood culture detection in time series data using a BiLSTM network—0
Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Fast Active Set Methods for Online Spike Inference from Calcium Imaging—0
Graphical Time Warping for Joint Alignment of Multiple Curves—0
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction—0
Complex-valued Gaussian Process Regression for Time Series Analysis—0
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models—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