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 2051–2075 of 6748 papers

TitleStatusHype
Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning—0
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts—0
Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices—0
A Review of Changepoint Detection Models—0
Adaptive Visibility Graph Neural Network and It's Application in Modulation Classification—0
Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data—0
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
Comparing Lexical Usage in Political Discourse across Diachronic Corpora—0
Comparative Study of Machine Learning Models for Stock Price Prediction—0
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition—0
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models—0
Early Detection of Forest Calamities in Homogeneous Stands -- Deep Learning Applied to Bark-Beetle Outbreaks—0
Early Discovery of Disappearing Entities in Microblogs—0
Comparative Computational Analysis of Global Structure in Canonical, Non-Canonical and Non-Literary Texts—0
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach—0
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak—0
A restricted eigenvalue condition for unit-root non-stationary data—0
Adaptive Transmit Waveform Design using Multi-Tone Sinusoidal Frequency Modulation—0
Comparative analysis of criteria for filtering time series of word usage frequencies—0
Compact representation of temporal processes in echosounder time series via matrix decomposition—0
A reproduction rate which perfectly fits Covid-19—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