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

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Showing 2901–2950 of 6748 papers

TitleStatusHype
Pattern Recognition in Vital Signs Using Spectrograms—0
Multimodal Meta-Learning for Time Series Regression—0
Local Exceptionality Detection in Time Series Using Subgroup Discovery—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
Bayesian forecast combination using time-varying features—0
Reconstructing a dynamical system and forecasting time series by self-consistent deep learning—0
Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst DetectionCode0
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data—0
Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms—0
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series—0
An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions—0
Synthetic Active Distribution System Generation via Unbalanced Graph Generative Adversarial Network—0
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators—0
Using Deep Learning to Correlate Reddit Posts with Economic Time Series During the COVID-19 Pandemic—0
Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps—0
Statistical learning method for predicting density-matrix based electron dynamics—0
Opinion Prediction with User FingerprintingCode0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—0
Analysis of complex circadian time series data using wavelets—0
Inference in heavy-tailed non-stationary multivariate time series—0
Spatio-temporal graph neural networks for multi-site PV power forecasting—0
Previsão dos preços de abertura, mínima e máxima de índices de mercados financeiros usando a associação de redes neurais LSTM—0
The interpretation of endobronchial ultrasound image using 3D convolutional neural network for differentiating malignant and benign mediastinal lesions—0
Otimizacao de pesos e funcoes de ativacao de redes neurais aplicadas na previsao de series temporais—0
Demand Forecasting in Smart Grid Using Long Short-Term Memory—0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
Snippet Policy Network for Multi-class Varied-length ECG Early Classification—0
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting—0
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals—0
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling—0
Inverse and Quanto Inverse Options in a Black-Scholes World—0
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale—0
A Signal Detection Scheme Based on Deep Learning in OFDM Systems—0
Automatic Detection Of Noise Events at Shooting Range Using Machine Learning—0
COVID-19 and the gig economy in PolandCode0
Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices—0
Tsformer: Time series Transformer for tourism demand forecasting—0
Recovering lost and absent information in temporal networksCode0
FNetAR: Mixing Tokens with Autoregressive Fourier TransformsCode0
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications—0
Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces—0
A Framework for Imbalanced Time-series ForecastingCode0
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies—0
Multiple species animal movements: network properties, disease dynamic and the impact of targeted control actionsCode0
Adaptive Inducing Points Selection For Gaussian Processes—0
High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series—0
Approximation Theory of Convolutional Architectures for Time Series Modelling—0
Improving COVID-19 Forecasting using eXogenous VariablesCode0
OnlineSTL: Scaling Time Series Decomposition by 100x—0
Topological Attention for Time Series Forecasting—0
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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