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

Showing 15011525 of 6748 papers

TitleStatusHype
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
Experimental study of time series forecasting methods for groundwater level predictionCode0
Experimental Study on Time Series Analysis of Lower Limb Rehabilitation Exercise Data Driven by Novel Model Architecture and Large ModelsCode0
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural NetworksCode0
A Joint-Entropy Approach To Time-series ClassificationCode0
Exoplanet Detection using Machine LearningCode0
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowCode0
EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal DataCode0
Event Detection via Probability Density Function RegressionCode0
Asset Price Forecasting using Recurrent Neural NetworksCode0
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourCode0
Evaluating Short-Term Forecasting of Multiple Time Series in IoT EnvironmentsCode0
Evaluating time series forecasting models: An empirical study on performance estimation methodsCode0
Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costsCode0
Evaluating Impact of Social Media Posts by Executives on Stock PricesCode0
COPER: Continuous Patient State PerceiverCode0
Order book model with herd behavior exhibiting long-range memoryCode0
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series ClassificationCode0
Evaluating Explanation Methods for Multivariate Time Series ClassificationCode0
Evaluating generation of chaotic time series by convolutional generative adversarial networksCode0
Concurrent Neural Network : A model of competition between times seriesCode0
Modelling stellar activity with Gaussian process regression networksCode0
Evolving-Graph Gaussian ProcessesCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
Fast Online Deconvolution of Calcium Imaging DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified