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 5551–5600 of 6748 papers

TitleStatusHype
Robustness of sentence length measures in written texts—0
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs—0
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series—0
Scalable Visualisation of Sentiment and Stance—0
Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition—0
EuroGames16: Evaluating Change Detection in Online ConversationCode0
Towards Experienced Anomaly Detector through Reinforcement Learning—0
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis—0
Learning from multivariate discrete sequential data using a restricted Boltzmann machine model—0
Deep learning approach to Fourier ptychographic microscopyCode0
Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series—0
Adaptive pooling operators for weakly labeled sound event detectionCode0
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint—0
Deep Learning for Predicting Asset ReturnsCode0
On The Complexity of Sparse Label Propagation—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Block-Structure Based Time-Series Models For Graph SequencesCode0
Econometric Modeling of Regional Electricity Spot Prices in the Australian Market—0
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers—0
Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery—0
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM—0
Taylor's law for Human Linguistic SequencesCode0
Calibration-free B0 correction of EPI data using structured low rank matrix recovery—0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
Deep Generative Networks For Sequence PredictionCode0
High Dimensional Time Series Generators—0
On Improving Deep Reinforcement Learning for POMDPs—0
Option Pricing under Fast-varying and Rough Stochastic Volatility—0
Detecting Concrete Abnormality Using Time-series Thermal Imaging and Supervised Learning—0
An interpretable LSTM neural network for autoregressive exogenous model—0
Predicting Cyber Events by Leveraging Hacker Sentiment—0
Causal Inference via Kernel Deviance Measures—0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Model identification for ARMA time series through convolutional neural networks—0
Anomaly Detection for Industrial Big Data—0
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis—0
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss—0
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space—0
Real-time Air Pollution prediction model based on Spatiotemporal Big dataCode1
micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observationsCode0
Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis—0
Spatial heterogeneity analyses identify limitations of epidemic alert systems: Monitoring influenza-like illness in France—0
Bag of Recurrence Patterns Representation for Time-Series Classification—0
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the YearsCode0
Epileptic Seizure Detection: A Deep Learning Approach—0
Kinetic Compressive Sensing—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Inferring network connectivity from event timing patternsCode0
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series ForecastingCode0
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos—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