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 551600 of 6748 papers

TitleStatusHype
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load ForecastingCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Ensembles of Localised Models for Time Series ForecastingCode1
AGNet: Weighing Black Holes with Deep LearningCode1
Feature-Based Time-Series Analysis in R using the theft PackageCode1
AGNet: Weighing Black Holes with Machine LearningCode1
ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Automated Evolutionary Approach for the Design of Composite Machine Learning PipelinesCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Automatic Change-Point Detection in Time Series via Deep LearningCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Time series forecasting with Gaussian Processes needs priorsCode1
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series ForecastingCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural NetworkCode1
Financial time series forecasting with multi-modality graph neural networkCode1
Backdoor Attacks on Time Series: A Generative ApproachCode1
Calibration of Google Trends Time SeriesCode1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization ApproachCode1
Axial-LOB: High-Frequency Trading with Axial AttentionCode1
Forecasting in Non-stationary Environments with Fuzzy Time SeriesCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
From Time Series to Networks in R with the ts2net PackageCode1
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning InfusionCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Gaussian Process Prior Variational AutoencodersCode1
Generalised Interpretable Shapelets for Irregular Time SeriesCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Generative ODE Modeling with Known UnknownsCode1
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor dataCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
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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