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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 5051–5100 of 6748 papers

TitleStatusHype
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves—0
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
Analysing the resilience of the European commodity production system with PyResPro, the Python Production Resilience package—0
Analysis and development of an automatic eCall for motorcycles: a one-class cepstrum approach—0
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models—0
Time Series Analysis and Modeling to Forecast: a Survey—0
Analysis of Advisor Portfolio using Multivariate Time Series and Cosine Similarity—0
Analysis of bank leverage via dynamical systems and deep neural networks—0
Analysis of bio-electro-chemical signals from passive sweat-based wearable electro-impedance spectroscopy (EIS) towards assessing blood glucose modulations—0
Analysis of Blink Rate Variability during reading and memory testing—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Analysis of chaotic dynamical systems with autoencoders—0
Analysis of complex circadian time series data using wavelets—0
Analysis of cyclical behavior in time series of stock market returns—0
Active Learning of Driving Scenario Trajectories—0
Analysis of EEG data using complex geometric structurization—0
Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting—0
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters—0
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting—0
Analysis, Online Estimation, and Validation of a Competing Virus Model—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions—0
Analyzing Time Series Changes of Correlation between Market Share and Concerns on Companies measured through Search Engine Suggests—0
An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations—0
An analysis of deep neural networks for predicting trends in time series data—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions—0
An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework—0
An Artificial Spiking Quantum Neuron—0
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series—0
An Attention Free Long Short-Term Memory for Time Series Forecasting—0
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth—0
An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows—0
An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics—0
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series—0
An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis—0
An Efficient Federated Distillation Learning System for Multi-task Time Series Classification—0
Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
An Empirical Study on How the Developers Discussed about Pandas Topics—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
An empirical study of neural networks for trend detection in time series—0
An Empirical Study of the L2-Boost technique with Echo State Networks—0
An End-to-End Model for Time Series Classification In the Presence of Missing Values—0
An Ensemble method for Content Selection for Data-to-text Systems—0
An Equilibrium Model for the Cross-Section of Liquidity Premia—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