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 2701–2725 of 6748 papers

TitleStatusHype
Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter—0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
For2For: Learning to forecast from forecasts—0
Forecastable Component Analysis (ForeCA)—0
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices—0
Comparative analysis of criteria for filtering time series of word usage frequencies—0
A reproduction rate which perfectly fits Covid-19—0
Forecasting and Analyzing the Military Expenditure of India Using Box-Jenkins ARIMA Model—0
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak—0
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach—0
Forecasting, capturing and activation of carbon-dioxide (CO_2): Integration of Time Series Analysis, Machine Learning, and Material Design—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Forecasting COVID- 19 cases using Statistical Models and Ontology-based Semantic Modelling: A real time data analytics approach—0
Causal Inference from Slowly Varying Nonstationary Processes—0
Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning—0
Forecasting Crude Oil Price Using Event Extraction—0
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM—0
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds—0
Forecasting Emerging Trends from Scientific Literature—0
Enhancing Energy System Models Using Better Load Forecasts—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