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 5401–5425 of 6748 papers

TitleStatusHype
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting—0
Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft—0
Unified recurrent network for many feature types—0
A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS—0
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk—0
Multi-task Learning for Financial Forecasting—0
Exploring the interpretability of LSTM neural networks over multi-variable data—0
A Short Survey of Topological Data Analysis in Time Series and Systems Analysis—0
Dataset: Rare Event Classification in Multivariate Time SeriesCode0
Temporal Relational Ranking for Stock PredictionCode0
Complex market dynamics in the light of random matrix theory—0
A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings—0
Unified recurrent neural network for many feature types—0
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia—0
Long-run dynamics of the U.S. patent classification system—0
Human activity recognition based on time series analysis using U-Net—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn—0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series—0
Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm—0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine—0
Similarity measure for Public Persons—0
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman FiltersCode0
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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