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 1051–1075 of 6748 papers

TitleStatusHype
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
Autoregressive Quantile Flows for Predictive Uncertainty Estimation—0
A New Look to Three-Factor Fama-French Regression Model using Sample Innovations—0
Distributional uncertainty of the financial time series measured by G-expectation—0
Autoregressive-Model-Based Methods for Online Time Series Prediction with Missing Values: an Experimental Evaluation—0
A new approach to the modeling of financial volumes—0
Autoregressive GNN-ODE GRU Model for Network Dynamics—0
A new approach for physiological time series—0
Adversarially learned anomaly detection for time series data—0
An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes—0
A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success—0
Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations—0
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning—0
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves—0
An Evaluation of Classification Methods for 3D Printing Time-Series Data—0
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes—0
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction—0
Automatic time-series phenotyping using massive feature extraction—0
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
An Evaluation of Classification and Outlier Detection Algorithms—0
Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data—0
ChunkFormer: Learning Long Time Series with Multi-stage Chunked Transformer—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