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 4401–4425 of 6748 papers

TitleStatusHype
Predicting Multidimensional Data via Tensor Learning—0
Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation—0
A review on outlier/anomaly detection in time series data—0
Timing Excess Returns A cross-universe approach to alpha—0
Autoencoder-based time series clustering with energy applications—0
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series—0
Finding manoeuvre motifs in vehicle telematics—0
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems—0
Time Series Alignment with Global InvariancesCode0
Learning CHARME models with neural networksCode0
Equivalence relations and L^p distances between time series with application to the Black Summer Australian bushfires—0
Unsupervised non-parametric change point detection in quasi-periodic signals—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Online change-point detection with kernels—0
Uncovering differential equations from data with hidden variables—0
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data—0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction—0
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models—0
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks—0
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks—0
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective—0
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network—0
Model Extraction Attacks against Recurrent Neural Networks—0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation—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