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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3276–3300 of 6748 papers

TitleStatusHype
Measuring frequency-dependent selection in culture—0
An investigation of higher order moments of empirical financial data and the implications to risk—0
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability—0
Isolating the impact of trading on grid frequency fluctuations—0
Intraday trading strategy based on time series and machine learning for Chinese stock market—0
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving NoiseCode0
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications—0
Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand—0
Cryptocurrency Dynamics: Rodeo or Ascot?—0
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery—0
Handling Missing Observations with an RNN-based Prediction-Update Cycle—0
Domain Specific Concept Drift Detectors for Predicting Financial Time Series—0
Markov Modeling of Time-Series Data using Symbolic Analysis—0
Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories—0
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version—0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Joint Parameter Discovery and Generative Modeling of Dynamic SystemsCode0
Learning Time Series from Scale Information—0
Lossless compression with state space models using bits back codingCode0
An Efficient Method for the Classification of Croplands in Scarce-Label RegionsCode0
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—0
Deep Time Series Models for Scarce Data—0
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transportCode0
Online Learning with Radial Basis Function Networks—0
Interpretable Feature Construction for Time Series Extrinsic Regression—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