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 5651–5675 of 6748 papers

TitleStatusHype
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
Construction of neural networks for realization of localized deep learning—0
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model—0
DeepMoTIon: Learning to Navigate Like Humans—0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Precision and Recall for Time SeriesCode0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Fast Robust Methods for Singular State-Space Models—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
M3Fusion: A Deep Learning Architecture for Multi-Scale/Modal/Temporal satellite data fusionCode0
SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction—0
Model-Based Clustering and Classification of Functional Data—0
A bootstrap test to detect prominent Granger-causalities across frequenciesCode0
Synthetic Control Methods and Big Data—0
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications—0
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes—0
Model Agnostic Time Series Analysis via Matrix EstimationCode0
Time Series Learning using Monotonic Logical Properties—0
Symmetric thermal optimal path and time-dependent lead-lag relationship: Novel statistical tests and application to UK and US real-estate and monetary policies—0
Diffusion Maps meet Nyström—0
Deep Multi-View Spatial-Temporal Network for Taxi Demand PredictionCode0
Deep learning algorithm for data-driven simulation of noisy dynamical system—0
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification—0
Structured low-rank matrix completion for forecasting in time series analysis—0
Learning Causally-Generated Stationary Time Series—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