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 6401–6450 of 6748 papers

TitleStatusHype
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy—0
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization—0
Efficient combination of pairswise feature networks—0
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors—0
Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series—0
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering—0
Efficient Kernel-based Subsequence Search for User Identification from Walking Activity—0
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data—0
Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis—0
Efficient Modeling and Forecasting of the Electricity Spot Price—0
Efficient multivariate sequence classification—0
Efficient Observation Time Window Segmentation for Administrative Data Machine Learning—0
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control—0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals—0
Efficient structure learning with automatic sparsity selection for causal graph processes—0
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging—0
Efficient Variational Bayes Learning of Graphical Models with Smooth Structural Changes—0
EigenNetworks—0
Eliciting Disease Data from Wikipedia Articles—0
Elucidation of time-dependent systems biology cell response patterns with time course network enrichment—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
Emotional Expression Classification using Time-Series Kernels—0
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting—0
Empirical analysis of daily cash flow time series and its implications for forecasting—0
Empirical facts characterizing banking crises: an analysis via binary time series—0
Empirical observations of ultraslow diffusion driven by the fractional dynamics in languages: Dynamical statistical properties of word counts of already popular words—0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction—0
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series—0
Empirics on the expressiveness of Randomized Signature—0
雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識 (Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments) [In Chinese]—0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Empowering Time Series Analysis with Large Language Models: A Survey—0
Emulating dynamic non-linear simulators using Gaussian processes—0
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding—0
End-to-end NILM System Using High Frequency Data and Neural Networks—0
End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker—0
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks—0
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
Energy consumption forecasting using a stacked nonparametric Bayesian approach—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Energy-Efficient Seizure Detection Suitable for low-power Applications—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks—0
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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