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 4751–4800 of 6748 papers

TitleStatusHype
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
Marginally-calibrated deep distributional regression—0
Market Comment Generation from Data with Noisy Alignments—0
Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study—0
Market forecasting using Hidden Markov Models—0
Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models—0
Market states: A new understanding—0
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems—0
Markovian Gaussian Process Variational Autoencoders—0
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments—0
Markov Modeling of Time-Series Data using Symbolic Analysis—0
Masked Multi-Step Multivariate Time Series Forecasting with Future Information—0
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand—0
Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale—0
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive—0
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition—0
Matched Illumination Waveforms using Multi-Tone Sinusoidal Frequency Modulation—0
Mathematical models of COVID-19 spread—0
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series—0
Maximally Divergent Intervals for Anomaly Detection—0
Maximum Entropy approach to multivariate time series randomization—0
Measurement of Anticipative Power of a Retina by Predictive Information—0
Measuring city-scale green infrastructure drawdown dynamics using internet-connected sensors in Detroit—0
Measuring frequency-dependent selection in culture—0
Measuring multiscaling in financial time-series—0
Measuring Wind Turbine Health Using Drifting Concepts—0
MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series—0
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis—0
MegazordNet: combining statistical and machine learning standpoints for time series forecasting—0
Memory and forecasting capacities of nonlinear recurrent networks—0
Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction—0
Memristive LSTM network hardware architecture for time-series predictive modeling problem—0
Mental State Classification Using Multi-graph Features—0
Merchant Category Identification Using Credit Card Transactions—0
Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification—0
Metadata-enhanced contrastive learning from retinal optical coherence tomography images—0
Meta-descent for Online, Continual Prediction—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Meta-Learning for Few-Shot Time Series Classification—0
Meta-Learning for Koopman Spectral Analysis with Short Time-series—0
Two-Step Meta-Learning for Time-Series Forecasting Ensemble—0
Metaphor Development in Public Discourse Using an ARIMA Time Series Analysis Approach—0
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction—0
Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions—0
Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method—0
Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis—0
Method of indirect estimation of default probability dynamics for industry-target segments according to the data of Bank of Russia—0
Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review—0
Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors—0
metricDTW: local distance metric learning in Dynamic Time Warping—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