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

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Showing 4301–4350 of 6748 papers

TitleStatusHype
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation modelCode0
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime—0
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods—0
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting—0
Topological Data Analysis in Text Classification: Extracting Features with Additive Information—0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
Correlated daily time series and forecasting in the M4 competitionCode0
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data—0
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks—0
Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis—0
Triad State Space Construction for Chaotic Signal Classification with Deep Learning—0
R-FORCE: Robust Learning for Random Recurrent Neural NetworksCode0
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction—0
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications—0
Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework—0
Scalable Deployment of AI Time-series Models for IoT—0
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes—0
The Internet of Things as a Deep Neural Network—0
Deep Markov Spatio-Temporal FactorizationCode0
One model is not enough: heterogeneity in cryptocurrencies' multifractal profiles—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Locating line and node disturbances in networks of diffusively coupled dynamical agents—0
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
BrazilDAM: A Benchmark dataset for Tailings Dam DetectionCode0
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions—0
Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting—0
PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction—0
Market states: A new understanding—0
Time series and machine learning to forecast the water quality from satellite data—0
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations—0
A CNN–LSTM model for gold price time-series forecasting—0
Hybrid Cryptocurrency Pump and Dump Detection—0
Phase-randomised Fourier transform model for the generation of synthetic wind speeds—0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm—0
A Time Series Approach To Player Churn and Conversion in Videogames—0
Deep learning-based sequential pattern mining for progressive database—0
Time Series Forecasting Using LSTM Networks: A Symbolic ApproachCode0
Nonlinear least-squares spline fitting with variable knots—0
Dynamic Spatiotemporal Graph Neural Network with Tensor Network—0
Linear-time inference for Gaussian Processes on one dimension—0
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models—0
A tail dependence-based MST and their topological indicators in modelling systemic risk in the European insurance sector—0
Time series modelling to forecast the confirmed and recovered cases of COVID-19—0
Progressive Growing of Neural ODEs—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets—0
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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