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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 3101–3150 of 6748 papers

TitleStatusHype
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Free congruence: an exploration of expanded similarity measures for time series data—0
Computer activity learning from system call time series—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
How Much Can A Retailer Sell? Sales Forecasting on Tmall—0
How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process—0
How Noisy Social Media Text, How Diffrnt Social Media Sources?—0
Extending the Range of Robust PCE Inflation Measures—0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
Fractional trends and cycles in macroeconomic time series—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory—0
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction—0
Fractional integration and cointegration—0
Human activity recognition based on time series analysis using U-Net—0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
A metric to compare the anatomy variation between image time series—0
Human Activity Recognition using Smartphone—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Fractional Growth Portfolio Investment—0
Human-like Time Series Summaries via Trend Utility Estimation—0
Human Motion Prediction via Pattern Completion in Latent Representation Space—0
Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems—0
Hybrid Backpropagation Parallel Reservoir Networks—0
Hybrid Cryptocurrency Pump and Dump Detection—0
Fractal Time Series Analysis of Social Network Activities—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Fractal structures in Adversarial Prediction—0
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface—0
Fractal approach towards power-law coherency to measure cross-correlations between time series—0
Hybrid Neural Networks for Learning the Trend in Time Series—0
Metaheuristics optimized feedforward neural networks for efficient stock price prediction—0
Hybrid Variational Autoencoder for Time Series Forecasting—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Hydroclimatic time series features at multiple time scales—0
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis—0
Hydroelectric Generation Forecasting with Long Short Term Memory (LSTM) Based Deep Learning Model for Turkey—0
Fractal analyses of networks of integrate-and-fire stochastic spiking neurons—0
FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting—0
Hyperinflation in Brazil, Israel, and Nicaragua revisited—0
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting—0
HyperTime: Implicit Neural Representation for Time Series—0
Hypotheses testing on infinite random graphs—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Composition Properties of Inferential Privacy for Time-Series Data—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