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 5351–5400 of 6748 papers

TitleStatusHype
Comparing Temporal Graphs Using Dynamic Time WarpingCode0
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach—0
Spikebench: An open benchmark for spike train time-series classificationCode0
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras—0
Discretizing Logged Interaction Data Biases Learning for Decision-Making—0
Mining Novel Multivariate Relationships in Time Series Data Using Correlation NetworksCode0
Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation—0
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse EffectsCode1
Learning Deep Representations from Clinical Data for Chronic Kidney Disease—0
Robust multivariate and functional archetypal analysis with application to financial time series analysis—0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces—0
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting—0
Unified recurrent network for many feature types—0
A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS—0
Exploring the interpretability of LSTM neural networks over multi-variable data—0
A Short Survey of Topological Data Analysis in Time Series and Systems Analysis—0
Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft—0
Dataset: Rare Event Classification in Multivariate Time SeriesCode0
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk—0
Multi-task Learning for Financial Forecasting—0
Temporal Relational Ranking for Stock PredictionCode0
Complex market dynamics in the light of random matrix theory—0
A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings—0
Unified recurrent neural network for many feature types—0
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia—0
Long-run dynamics of the U.S. patent classification system—0
Human activity recognition based on time series analysis using U-Net—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn—0
Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series—0
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal DynamicsCode0
Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm—0
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine—0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
Similarity measure for Public Persons—0
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman FiltersCode0
Random Warping Series: A Random Features Method for Time-Series EmbeddingCode0
A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors—0
Anomaly Detection with Generative Adversarial Networks for Multivariate Time SeriesCode0
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
Temporal Pattern Attention for Multivariate Time Series ForecastingCode0
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product NetworksCode0
Deep learning for time series classification: a reviewCode2
Memristive LSTM network hardware architecture for time-series predictive modeling problem—0
Order book model with herd behavior exhibiting long-range memoryCode0
Constrained Generation of Semantically Valid Graphs via Regularizing Variational AutoencodersCode0
Revisiting Inaccuracies of Time Series Averaging under Dynamic Time Warping—0
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations—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