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 1401–1425 of 6748 papers

TitleStatusHype
Finding Patterns in Visualized Data by Adding Redundant Visual Information—0
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering—0
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)Code1
Deep Generators on Commodity Markets; application to Deep Hedging—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Topological Hidden Markov Models—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse ObservationsCode1
Are Transformers Effective for Time Series Forecasting?Code4
Learning the spatio-temporal relationship between wind and significant wave height using deep learningCode1
Sparse Graph Learning from Spatiotemporal Time SeriesCode1
Conformal Prediction Intervals with Temporal DependenceCode0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network GenerationCode1
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time SeriesCode0
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Forecasting Multilinear Data via Transform-Based Tensor Autoregression—0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization—0
Forecasting of Non-Stationary Sales Time Series Using Deep Learning—0
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies—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