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 5951–5975 of 6748 papers

TitleStatusHype
Learning to Generate Market Comments from Stock Prices—0
Modeling Sub-Event Dynamics in First-Person Action Recognition—0
Probabilistic Temporal Subspace Clustering—0
Hybrid Neural Networks for Learning the Trend in Time Series—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
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data—0
Introducing Data Primitives: Data Formats for the SKED Framework—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
Infinite Mixture Model of Markov Chains—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Analysis of cyclical behavior in time series of stock market returns—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data—0
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm—0
Time Series Data Cleaning: From Anomaly Detection to Anomaly RepairingCode0
Time Series Using Exponential Smoothing CellsCode0
Driver Identification Using Automobile Sensor Data from a Single Turn—0
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANsCode0
Bayesian LSTMs in medicine—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