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 401450 of 6748 papers

TitleStatusHype
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriesCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature ExtractionCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Adaptive Conformal Predictions for Time SeriesCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
HIVE-COTE 2.0: a new meta ensemble for time series classificationCode1
The Signature Kernel is the solution of a Goursat PDECode1
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time seriesCode1
Conditional GAN for timeseries generationCode1
Human Activity Segmentation Challenge @ ECML/PKDD’23Code1
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
Combating Distribution Shift for Accurate Time Series Forecasting via HypernetworksCode1
DEPTS: Deep Expansion Learning for Periodic Time Series ForecastingCode1
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
Improving Deep Learning Interpretability by Saliency Guided TrainingCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
Conformal Time-series ForecastingCode1
Conformal prediction set for time-seriesCode1
Improving S&P stock prediction with time series stock similarityCode1
Inductive Graph Neural Networks for Spatiotemporal KrigingCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life predictionCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
Interpretable Models for Granger Causality Using Self-explaining Neural NetworksCode1
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural NetworksCode1
An Empirical Evaluation of Time-Series Feature SetsCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
Continuous Latent Process FlowsCode1
Evaluation of post-hoc interpretability methods in time-series classificationCode1
Differentiable Divergences Between Time SeriesCode1
An Empirical Survey of Data Augmentation for Time Series Classification with Neural NetworksCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Continuous-Time Deep Glioma Growth ModelsCode1
Are we certain it's anomalous?Code1
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive ModelingCode1
Adversarial Attacks on Time SeriesCode1
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence CaseCode1
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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