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 2776–2800 of 6748 papers

TitleStatusHype
Complexity Measures and Features for Times Series classification—0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Generative Adversarial Networks in finance: an overview—0
Causal Inference from Slowly Varying Nonstationary Processes—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Forecast with Forecasts: Diversity Matters—0
Foreign Exchange Market Performance: Evidence from Bivariate Time Series Approach—0
Foreign exchange risk premia: from traditional to state-space analyses—0
Forestry digital twin with machine learning in Landsat 7 data—0
Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information—0
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification—0
Enhancing Energy System Models Using Better Load Forecasts—0
Foundation Models for Time Series: A Survey—0
Foundations of Sequence-to-Sequence Modeling for Time Series—0
Log-PDE Methods for Rough Signature Kernels—0
Fourier-RNNs for Modelling Noisy Physics Data—0
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting—0
Fractal analyses of networks of integrate-and-fire stochastic spiking neurons—0
Fractal approach towards power-law coherency to measure cross-correlations between time series—0
Fractal structures in Adversarial Prediction—0
Fractal Time Series Analysis of Social Network Activities—0
Fractional Growth Portfolio Investment—0
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning—0
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting—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