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 501–525 of 6748 papers

TitleStatusHype
Towards a Rigorous Evaluation of Time-series Anomaly DetectionCode1
NTS-NOTEARS: Learning Nonparametric DBNs With Prior KnowledgeCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life predictionCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
Transformer Networks for Data Augmentation of Human Physical Activity RecognitionCode1
MrSQM: Fast Time Series Classification with Symbolic RepresentationsCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
TCCT: Tightly-Coupled Convolutional Transformer on Time Series ForecastingCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
S&P 500 Stock Price Prediction Using Technical, Fundamental and Text DataCode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
AGNet: Weighing Black Holes with Deep LearningCode1
Data-driven discovery of intrinsic dynamicsCode1
Predicting in-hospital mortality by combining clinical notes with time-series dataCode1
Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia ClassificationCode1
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural NetworksCode1
Temporal Dependencies in Feature Importance for Time Series PredictionsCode1
Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-SeriesCode1
Heteroscedastic Temporal Variational Autoencoder For Irregular Time SeriesCode1
Generative adversarial networks in time series: A survey and taxonomyCode1
Long-term series forecasting with Query Selector -- efficient model of sparse attentionCode1
STRODE: Stochastic Boundary Ordinary Differential EquationCode1
Neural Contextual Anomaly Detection for Time SeriesCode1
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention NetworksCode1
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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