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 4876–4900 of 6748 papers

TitleStatusHype
Analysis and development of an automatic eCall for motorcycles: a one-class cepstrum approach—0
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIICode0
A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecastingCode0
Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
Convolutional Reservoir Computing for World ModelsCode0
Clustering Activity-Travel Behavior Time Series using Topological Data Analysis—0
Meta-descent for Online, Continual Prediction—0
Remaining Useful Lifetime Prediction via Deep Domain Adaptation—0
End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Dynamical Systems as Temporal Feature Spaces—0
Quick, Stat!: A Statistical Analysis of the Quick, Draw! Dataset—0
Quant GANs: Deep Generation of Financial Time Series—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
An Artificial Spiking Quantum Neuron—0
The Use of Gaussian Processes in System Identification—0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks—0
A machine learning framework for computationally expensive transient modelsCode0
Change point detection for graphical models in the presence of missing valuesCode0
Forecasting Time Series with VARMA Recursions on Graphs—0
Time series cluster kernels to exploit informative missingness and incomplete label information—0
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network—0
GP-VAE: Deep Probabilistic Time Series ImputationCode0
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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