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 3026–3050 of 6748 papers

TitleStatusHype
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
Dynamic and Thermodynamic Models of Adaptation—0
Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?Code0
Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference—0
Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series—0
Missing Value Imputation on Multidimensional Time SeriesCode1
A Novel CNN-LSTM-based Approach to Predict Urban Expansion—0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Variance Reduced Training with Stratified Sampling for Forecasting Models—0
A Spectral Enabled GAN for Time Series Data Generation—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
CLPVG: Circular limited penetrable visibility graph as a new network model for time series—0
The Kernel Trick for Nonlinear Factor Modeling—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
Automated data-driven approach for gap filling in the time series using evolutionary learningCode0
Unsupervised dynamic modeling of medical image transformationCode0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
Forecasting high-frequency financial time series: an adaptive learning approach with the order book data—0
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion—0
Adaptable Hamiltonian neural networks—0
Learning orbital dynamics of binary black hole systems from gravitational wave measurementsCode0
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff—0
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time SeriesCode1
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services—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