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 2201–2225 of 6748 papers

TitleStatusHype
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series—0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
Deep Temporal Contrastive Clustering—0
A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain—0
Deep Symbolic Representation Learning for Heterogeneous Time-series Classification—0
Deep Switch Networks for Generating Discrete Data and Language—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs—0
Deep Subspace Encoders for Nonlinear System Identification—0
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction—0
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation—0
Deep State Space Models for Time Series Forecasting—0
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
Deep Signature Statistics for Likelihood-free Time-series Models—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
A new measure between sets of probability distributions with applications to erratic financial behavior—0
Deep Sequence Modeling: Development and Applications in Asset Pricing—0
Deep Sequence Learning for Accurate Gestational Age Estimation from a \$25 Doppler Device—0
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
Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment—0
Deep Reinforcement Learning for Trading—0
Distributional uncertainty of the financial time series measured by G-expectation—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