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 56015650 of 6748 papers

TitleStatusHype
Nonlinear Deconvolution by Sampling Biophysically Plausible Hemodynamic Models0
An Incremental Boolean Tensor Factorization approach to model Change Patterns of Objects in Images0
Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligenceCode0
Chatter Classification in Turning Using Machine Learning and Topological Data Analysis0
Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk ModelingCode1
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks0
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples0
Seglearn: A Python Package for Learning Sequences and Time SeriesCode0
Reservoir computing approaches for representation and classification of multivariate time seriesCode0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs0
Universal features of price formation in financial markets: perspectives from Deep Learning0
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Coordinating users of shared facilities via data-driven predictive assistants and game theory0
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM0
Theory and Algorithms for Forecasting Time Series0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology0
Adaptive Kernel Estimation of the Spectral Density with Boundary Kernel Analysis0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
Sales forecasting using WaveNet within the framework of the Kaggle competition0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting0
Construction of neural networks for realization of localized deep learning0
DeepMoTIon: Learning to Navigate Like Humans0
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model0
Precision and Recall for Time SeriesCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Improving Optimization for Models With Continuous Symmetry Breaking0
Fast Robust Methods for Singular State-Space Models0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization0
M3Fusion: A Deep Learning Architecture for Multi-Scale/Modal/Temporal satellite data fusionCode0
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
Model-Based Clustering and Classification of Functional Data0
Synthetic Control Methods and Big Data0
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications0
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes0
Model Agnostic Time Series Analysis via Matrix EstimationCode0
Symmetric thermal optimal path and time-dependent lead-lag relationship: Novel statistical tests and application to UK and US real-estate and monetary policies0
Time Series Learning using Monotonic Logical Properties0
Diffusion Maps meet Nyström0
Deep Multi-View Spatial-Temporal Network for Taxi Demand PredictionCode0
Learning Causally-Generated Stationary Time Series0
Structured low-rank matrix completion for forecasting in time series analysis0
Deep learning algorithm for data-driven simulation of noisy dynamical system0
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification0
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks0
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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