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 1251–1275 of 6748 papers

TitleStatusHype
Semi-unsupervised Learning for Time Series ClassificationCode0
Variations on two-parameter families of forecasting functions: seasonal/nonseasonal Models, comparison to the exponential smoothing and ARIMA models, and applications to stock market data—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer—0
Adaptive deep learning for nonlinear time series models—0
Don't overfit the history -- Recursive time series data augmentation—0
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation—0
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical SystemsCode1
Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series—0
Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding—0
Input Sequence and Parameter Estimation in Impulsive Biomedical Models—0
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures—0
Modeling Randomly Walking Volatility with Chained Gamma Distributions—0
Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial NetworksCode0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Continuous Sign Language Recognition via Temporal Super-Resolution Network—0
Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units—0
Multivariate Time Series Anomaly Detection with Few Positive SamplesCode1
Stock Performance Evaluation for Portfolio Design from Different Sectors of the Indian Stock Market—0
A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding—0
Simulating financial time series using attention—0
Rapid training of quantum recurrent neural networksCode0
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems—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