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 4301–4325 of 6748 papers

TitleStatusHype
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Online Learning of the Kalman Filter with Logarithmic Regret—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Timing Excess Returns A cross-universe approach to alpha—0
On the statistics of scaling exponents and the Multiscaling Value at Risk—0
Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation—0
Gaussian process imputation of multiple financial series—0
Selecting time-series hyperparameters with the artificial jackknifeCode1
Exact Indexing of Time Series under Dynamic Time Warping—0
Predicting Multidimensional Data via Tensor Learning—0
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series ForecastingCode1
A review on outlier/anomaly detection in time series data—0
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems—0
Finding manoeuvre motifs in vehicle telematics—0
Time Series Alignment with Global InvariancesCode0
Autoencoder-based time series clustering with energy applications—0
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series—0
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Improving S&P stock prediction with time series stock similarityCode1
Learning CHARME models with neural networksCode0
Online change-point detection with kernels—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Meta-learning framework with applications to zero-shot time-series forecastingCode1
Unsupervised non-parametric change point detection in quasi-periodic signals—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