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 4176–4200 of 6748 papers

TitleStatusHype
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
The fractal time growth of COVID-19 pandemic: an accurate self-similar model, and urgent conclusions—0
Financial Market Trend Forecasting and Performance Analysis Using LSTM—0
Machine Learning Algorithms for Financial Asset Price Forecasting—0
Adversarial Attacks on Multivariate Time Series—0
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Pruned Wasserstein Index Generation Model and wigpy PackageCode0
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime—0
Difference Attention Based Error Correction LSTM Model for Time Series Prediction—0
High-dimensional mixed-frequency IV regression—0
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation modelCode0
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods—0
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting—0
Topological Data Analysis in Text Classification: Extracting Features with Additive Information—0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction—0
Correlated daily time series and forecasting in the M4 competitionCode0
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis—0
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks—0
Triad State Space Construction for Chaotic Signal Classification with Deep Learning—0
FastDTW is approximate and Generally Slower than the Algorithm it ApproximatesCode1
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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