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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

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Showing 2751–2800 of 6748 papers

TitleStatusHype
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
Forecasting Player Behavioral Data and Simulating in-Game Events—0
A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy—0
Forecasting Realized Volatility Matrix With Copula-Based Models—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship—0
Forecasting Short-term load using Econometrics time series model with T-student Distribution—0
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management—0
Local-Global Methods for Generalised Solar Irradiance Forecasting—0
Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities—0
Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach—0
Forecasting the abnormal events at well drilling with machine learning—0
Forecasting The JSE Top 40 Using Long Short-Term Memory Networks—0
A review of predictive uncertainty estimation with machine learning—0
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study—0
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning—0
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Compensatory model for quantile estimation and application to VaR—0
Forecasting time series with encoder-decoder neural networks—0
Forecasting Time Series with VARMA Recursions on Graphs—0
Forecasting trends with asset prices—0
Forecasting under Long Memory and Nonstationarity—0
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization—0
Forecasting with a Panel Tobit Model—0
Causal Inference via Kernel Deviance Measures—0
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