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 3101–3150 of 6748 papers

TitleStatusHype
Covariance-engaged Classification of Sets via Linear Programming—0
House Price Prediction Using LSTM—0
雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識 (Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments) [In Chinese]—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
How macroscopic laws describe complex dynamics: asymptomatic population and CoviD-19 spreading—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
Empirics on the expressiveness of Randomized Signature—0
How to Identify Investor's types in real financial markets by means of agent based simulation—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
Causal Discovery from Sparse Time-Series Data Using Echo State Network—0
Phase-randomised Fourier transform model for the generation of synthetic wind speeds—0
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events—0
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction—0
Huber Additive Models for Non-stationary Time Series Analysis—0
Human activity recognition based on time series analysis using U-Net—0
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
Human Activity Recognition using Smartphone—0
Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction—0
Causal Discovery from Conditionally Stationary Time Series—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
COVID-19: Tail Risk and Predictive Regressions—0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface—0
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting—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
Empirical observations of ultraslow diffusion driven by the fractional dynamics in languages: Dynamical statistical properties of word counts of already popular words—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
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
Agriculture Credit and Economic Growth in Bangladesh: A Time Series Analysis—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
3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting—0
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior—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