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 1376–1400 of 6748 papers

TitleStatusHype
Forecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms—0
Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
Constraints on parameter choices for successful reservoir computing—0
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning RulesCode1
Learning code summarization from a small and local dataset—0
Generating Sparse Counterfactual Explanations For Multivariate Time SeriesCode0
Data Imputation for Multivariate Time Series Sensor Data with Large Gaps of Missing DataCode0
Sentiment Analysis of Homeric Text: The 1st Book of Iliad—0
Visualizing Parliamentary Speeches as Networks: the DYLEN Tool—0
OmniXAI: A Library for Explainable AICode2
SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks—0
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction—0
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting—0
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsCode0
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasksCode0
Temporal Multiresolution Graph Neural Networks For Epidemic PredictionCode0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
CHALLENGER: Training with Attribution Maps—0
Non-stationary Transformers: Exploring the Stationarity in Time Series ForecastingCode2
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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