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

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Showing 2526–2550 of 6748 papers

TitleStatusHype
Exploiting Topic based Twitter Sentiment for Stock Prediction—0
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants—0
Exploring asymmetric multifractal cross-correlations of price-volatility and asymmetric volatility dynamics in cryptocurrency markets—0
Exploring Financial Networks Using Quantile Regression and Granger Causality—0
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach—0
Classifying Contaminated Cell Cultures using Time Series Features—0
Exploring Long-Term Temporal Trends in the Use of Multiword Expressions—0
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack—0
Exploring Predictive States via Cantor Embeddings and Wasserstein Distance—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy—0
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response—0
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications—0
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
Exploring the representativeness of the M5 competition data—0
Exploring time-series motifs through DTW-SOM—0
Exploring Transfer Function Nonlinearity in Echo State Networks—0
Exponential inequalities for nonstationary Markov Chains—0
Exposing the Impact of GenAI for Cybercrime: An Investigation into the Dark Side—0
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection—0
Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint—0
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis—0
Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices—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