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 5926–5950 of 6748 papers

TitleStatusHype
Tailoring Artificial Neural Networks for Optimal LearningCode0
Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov ModelCode0
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Structured Black Box Variational Inference for Latent Time Series Models—0
Causal Consistency of Structural Equation Models—0
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference—0
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific FeaturesCode0
Hybrid Neural Networks for Learning the Trend in Time Series—0
Probabilistic Temporal Subspace Clustering—0
Modeling Sub-Event Dynamics in First-Person Action Recognition—0
Learning to Generate Market Comments from Stock Prices—0
Empirical analysis of daily cash flow time series and its implications for forecasting—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
Forecasting and Granger Modelling with Non-linear Dynamical DependenciesCode0
Introducing Data Primitives: Data Formats for the SKED Framework—0
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data—0
Reservoir Computing on the Hypersphere—0
TimeNet: Pre-trained deep recurrent neural network for time series classificationCode0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
Infinite Mixture Model of Markov Chains—0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Analysis of cyclical behavior in time series of stock market returns—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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