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 64516500 of 6748 papers

TitleStatusHype
Joint community and anomaly tracking in dynamic networks0
GEFCOM 2014 - Probabilistic Electricity Price Forecasting0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals0
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence0
Optimal model-free prediction from multivariate time series0
Time Series Classification using the Hidden-Unit Logistic Model0
Robust Structured Low-Rank Approximation on the Grassmannian0
An Ensemble method for Content Selection for Data-to-text Systems0
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series0
backShift: Learning causal cyclic graphs from unknown shift interventionsCode0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models0
Optimal change point detection in Gaussian processes0
Toward a generic representation of random variables for machine learning0
Imaging Time-Series to Improve Classification and ImputationCode1
Efficient combination of pairswise feature networks0
A Critical Review of Recurrent Neural Networks for Sequence LearningCode0
Sufficient Forecasting Using Factor Models0
Times series averaging from a probabilistic interpretation of time-elastic kernel0
Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices0
Affine and Regional Dynamic Time Warpng0
Regulating Greed Over Time in Multi-Armed BanditsCode0
Long-range memory and multifractality in gold markets0
Multifractal characterization of gold market: a multifractal detrended fluctuation analysis0
Forecasting Financial Extremes: A Network Degree Measure of Super-exponential Growth0
Cellular reprogramming dynamics follow a simple one-dimensional reaction coordinate0
Filter characteristics in image decomposition with singular spectrum analysis0
Cats & Co: Categorical Time Series Coclustering0
Autoencoding Time Series for Visualisation0
Optimal Time-Series Motifs0
Kernel Spectral Clustering and applications0
Market forecasting using Hidden Markov Models0
Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models0
Differential Recurrent Neural Networks for Action Recognition0
A Bayesian approach for structure learning in oscillating regulatory networks0
A new approach for physiological time series0
Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes0
Forecasting trends with asset prices0
Estimating the Algorithmic Complexity of Stock Markets0
Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces0
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series0
Eliciting Disease Data from Wikipedia Articles0
Structure Learning of Partitioned Markov Networks0
Sparse plus low-rank autoregressive identification in neuroimaging time series0
A Bayesian Approach to Sparse plus Low rank Network Identification0
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories0
Fusing Continuous-valued Medical Labels using a Bayesian Model0
Asymmetric Distributions from Constrained Mixtures0
Ultra-Fast Shapelets for Time Series Classification0
Scalable Discovery of Time-Series Shapelets0
Ranking and significance of variable-length similarity-based time series motifs0
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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