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 3826–3850 of 6748 papers

TitleStatusHype
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry—0
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry—0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry—0
Generating Trading Signals by ML algorithms or time series ones?—0
Modeling Financial Time Series using LSTM with Trainable Initial Hidden StatesCode1
SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks—0
A unified machine learning approach to time series forecasting applied to demand at emergency departments—0
GeoStat Representations of Time Series for Fast Classification—0
Rewiring the Transformer with Depth-Wise LSTMs—0
Identifying Latent Stochastic Differential EquationsCode0
On the variability of functional connectivity and network measures in source-reconstructed EEG time-series—0
Multi-future Merchant Transaction Prediction—0
Prediction of Traffic Flow via Connected Vehicles—0
Graph Signal Processing: Vertex Multiplication—0
Fast Variational Learning in State-Space Gaussian Process ModelsCode1
Learning Differential Equations that are Easy to SolveCode1
Long Short-Term Memory Spiking Networks and Their ApplicationsCode1
Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks—0
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data—0
Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach—0
Accuracy of neural networks for the simulation of chaotic dynamics: precision of training data vs precision of the algorithm—0
Few-Shot One-Class Classification via Meta-LearningCode1
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands—0
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activityCode1
Show:102550
← PrevPage 154 of 270Next →
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