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 4776–4800 of 6748 papers

TitleStatusHype
Real Time Trajectory Prediction Using Deep Conditional Generative ModelsCode0
Estimating Granger Causality with Unobserved Confounders via Deep Latent-Variable Recurrent Neural Network—0
Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information—0
Recovery of Future Data via Convolution Nuclear Norm MinimizationCode0
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)Code0
State Drug Policy Effectiveness: Comparative Policy Analysis of Drug Overdose Mortality—0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data—0
Reusing Convolutional Activations from Frame to Frame to Speed up Training and InferenceCode0
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future DirectionsCode0
Inferring species interactions using Granger causality and convergent cross mappingCode0
Gaussian mixture model decomposition of multivariate signals—0
Interdependency between the Stock Market and Financial News—0
Multilingual Dynamic Topic Model—0
Inspection of methods of empirical mode decomposition—0
Machine Learning and the Internet of Things Enable Steam Flood Optimization for Improved Oil Production—0
A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
Feedback System Neural Networks for Inferring Causality in Directed Cyclic Graphs—0
Early Classification for Agricultural Monitoring from Satellite Time Series—0
The Wiki Music dataset: A tool for computational analysis of popular music—0
Marginally-calibrated deep distributional regression—0
Variationally Inferred Sampling Through a Refined Bound for Probabilistic ProgramsCode0
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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