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 6101–6125 of 6748 papers

TitleStatusHype
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time SeriesCode0
Bayesian Spillover Graphs for Dynamic NetworksCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
Learning Sequential Latent Variable Models from Multimodal Time Series DataCode0
Generative Adversarial Network for Future Hand Segmentation from Egocentric VideoCode0
Balanced Graph Structure Learning for Multivariate Time Series ForecastingCode0
Stock Movement Prediction from Tweets and Historical PricesCode0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Tight lower bounds for Dynamic Time WarpingCode0
Generating Sparse Counterfactual Explanations For Multivariate Time SeriesCode0
A Statistical Investigation of Long Memory in Language and MusicCode0
Stock Price Prediction Based on Natural Language ProcessingCode0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
Learning the Dynamics of Sparsely Observed Interacting SystemsCode0
Generating Reliable Process Event Streams and Time Series Data based on Neural NetworksCode0
Convolutional Reservoir Computing for World ModelsCode0
Dependent Latent Class ModelsCode0
Asset Price Forecasting using Recurrent Neural NetworksCode0
Bayesian Online Changepoint DetectionCode0
Convolutional Conditional Neural ProcessesCode0
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open QuestionsCode0
Bayesian Nonparametric Spectral EstimationCode0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
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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