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 4251–4300 of 6748 papers

TitleStatusHype
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series—0
Forecasting Multi-Dimensional Processes over Graphs—0
Exploring time-series motifs through DTW-SOM—0
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future—0
Random thoughts about Complexity, Data and Models—0
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown—0
Hidden Markov Neural Networks—0
Asset Pricing with General Transaction Costs: Theory and Numerics—0
Characterizing the memory capacity of transmon qubit reservoirs—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Minority Oversampling for Imbalanced Time Series ClassificationCode0
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0—0
The leverage effect and other stylized facts displayed by Bitcoin returns—0
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems—0
On Error Correction Neural Networks for Economic Forecasting—0
Clustering Time Series Data through Autoencoder-based Deep Learning Models—0
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
Flexible Transmitter Network—0
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)—0
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs—0
Probabilistic Spatial Transformer NetworksCode0
Challenges in Forecasting Malicious Events from Incomplete Data—0
Forecasting in multivariate irregularly sampled time series with missing values—0
TSInsight: A local-global attribution framework for interpretability in time-series data—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack—0
Generating Similarity Map for COVID-19 Transmission Dynamics with Topological Autoencoder—0
Spatio-Temporal Graph Structure Learning for Traffic Forecasting—0
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units—0
Enhance the performance of navigation: A two-stage machine learning approach—0
Time-varying volatility in Bitcoin market and information flow at minute-level frequency—0
Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—0
Stationarity of Time-Series on Graph via Bivariate Translation Invariance—0
Machine Learning Algorithms for Financial Asset Price Forecasting—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
Adversarial Attacks on Multivariate Time Series—0
When Ramanujan meets time-frequency analysis in complicated time series analysis—0
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
Financial Market Trend Forecasting and Performance Analysis Using LSTM—0
The fractal time growth of COVID-19 pandemic: an accurate self-similar model, and urgent conclusions—0
High-dimensional mixed-frequency IV regression—0
Pruned Wasserstein Index Generation Model and wigpy PackageCode0
Difference Attention Based Error Correction LSTM Model for Time Series Prediction—0
Show:102550
← PrevPage 86 of 135Next →
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