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

Showing 45014550 of 6748 papers

TitleStatusHype
Continuous Meta-Learning without TasksCode0
On the Metrics and Adaptation Methods for Domain Divergences of sEMG-based Gesture RecognitionCode0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks0
Water Supply Prediction Based on Initialized Attention Residual Network0
A posteriori Trading-inspired Model-free Time Series Segmentation0
Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-190
GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining0
CORAD: Correlation-Aware Compression of Massive Time Series using Sparse Dictionary CodingCode0
Containment strategies and statistical measures for the control of Bovine Viral Diarrhea spread in livestock trade networks0
Modified Computation of Correlation Integral for Analyzing Epileptic Signals0
Towards Better Forecasting by Fusing Near and Distant Future VisionsCode0
Deteção de estruturas permanentes a partir de dados de séries temporais Sentinel 1 e 20
Classification des Séries Temporelles Incertaines par Transformation Shapelet0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Diffeomorphic Temporal Alignment NetsCode0
Hidden Markov Model: Tutorial0
An empirical study of neural networks for trend detection in time series0
Machine learning models show similar performance to Renewables.ninja for generation of long-term wind power time series even without location information0
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers0
Data Exploration and Validation on dense knowledge graphs for biomedical research0
Automatic Financial Feature Construction0
Open-domain Event Extraction and Embedding for Natural Gas Market PredictionCode0
Improved PAC-Bayesian Bounds for Linear Regression0
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms0
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization0
Reinforcement Learning with Convolutional Reservoir Computing0
Warped Input Gaussian Processes for Time Series ForecastingCode0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data0
Regression with Uncertainty Quantification in Large Scale Complex Data0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia0
Work-in-progress: a deep learning strategy for I/O scheduling in storage systemsCode0
Differential Bayesian Neural Nets0
Capacity of the covariance perceptron0
Shallow RNN: Accurate Time-series Classification on Resource Constrained DevicesCode0
Time-series Generative Adversarial Networks0
Machine learning applications in time series hierarchical forecasting0
Latent Ordinary Differential Equations for Irregularly-Sampled Time SeriesCode0
Time-series Generative Adversarial Networks0
Learning Representations for Time Series ClusteringCode0
Perceiving the arrow of time in autoregressive motion0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
ODE2VAE: Deep generative second order ODEs with Bayesian neural networksCode0
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters0
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-20190
On model selection for scalable time series forecasting in transport networks0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified