Robust tests for time series comparison based on Laplace periodograms

Jin, Lei

doi:10.1016/j.csda.2021.107223

Cited by 3 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…λ means the long-term inter-relation scale coefficient. λ > 0.5 signifies the positive long-term inter-relation between the two series and vice versa [27,28]. e DCCA method can be extended to study the long-term inter-relation between two unsteady signals.…”

Section: Multifractal Analysis Methods Of Time Seriesmentioning

confidence: 99%

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Miao

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The present work aims to analyze the time-series data (TSD) from movies and support constructing the movie recommendation system. Referencing the Internet of Things (IoT) technology as the framework, a time-series data analysis system for movies is built based on the recurrent neural network (RNN) and multifractal detrended mobility cross-correlation analysis (MF-DCCA) method. First, the traditional RNN model is improved by replacing the conventional convolution operation with spatial adaptive convolution. Specifically, an additional convolution layer is used to obtain the position parameters required for adaptive convolution to improve the model performance to capture the characteristics of spatial-temporal transformation. Then, the MF-DCCA method is optimized to reduce the interference of noise signals to the analysis processing of TSD from movies. Finally, the TSD analysis system is tested for performance verification. The test results indicate that the method proposed here has outstanding stability and runs smoothly. When the prediction scheme is long short-term memory (LSTM) (L = 20), the similarity of the LSTM (L = 20) network under one frame is 0.977; the similarity of the LSTM (L = 20) network under nine frames is 0.727. This system provides a specific idea for applying the RNN model and MF-DCCA method in analyzing TSD from movies.

show abstract

Section: Multifractal Analysis Methods Of Time Seriesmentioning

confidence: 99%

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Miao

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…As a result of the application carried out using S&P 500 index daily return data, it is revealed that the quantile frequency analysis provides extra contributions, compared to the classical methods, to understanding the goodness of fit with regard to financial models, serial dependence and regime shifts in financial stochastic processes. Jin (2021) developed tests aiming to detect the dynamics of different time series using the Laplace periodogram. For the vibration-based damage detection of a mechanical system, four vibration variables were used.…”

Section: Literaturementioning

confidence: 99%

Investigation of Serial Dependence Asymmetry and Time Irreversibility in Stock Market Returns of Mist Countries Using the Quantile Periodogram

BEKAR

2023

eab

View full text Add to dashboard Cite

The stock market indices of the countries are indicators that provide information about the countries' economies and financial stability. The aim of the study is to determine the similarities and differences in the stock market index return behaviors for Mexico, Indonesia, South Korea and Türkiye, which constitute the MIST country group. For this purpose, the spectral density kernel estimator "Quantile Periodogram" was used. The reason why this estimator is preferred is that it allows the investigation of serial dependence at different quantiles-frequencies and it is robust to outliers frequently encountered in return series, heavy-tailed distribution and changes in the distribution at high moments. The asymmetry of the serial dependence in different quantiles-frequencies and time-irreversibility which gives information about whether the financial series behavior is predictable or not, were analyzed with the quantile periodogram. According to the findings, Türkiye is the most preferred country by financial investors among MIST countries, while Mexico is the least preferred. Secondly, it is seen that the long-term behavior predictability of the returns has increased. This means that returns are more stable in the long run. When the findings are evaluated collectively, it is concluded that MIST countries are attractive for long-term financial investment.

show abstract

Scalable Methods for Multiple Time Series Comparison in Second Order Dynamics

Jin,

2024

Technometrics

View full text Add to dashboard Cite

Robust tests for time series comparison based on Laplace periodograms

Cited by 3 publications

References 31 publications

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Investigation of Serial Dependence Asymmetry and Time Irreversibility in Stock Market Returns of Mist Countries Using the Quantile Periodogram

Scalable Methods for Multiple Time Series Comparison in Second Order Dynamics

Contact Info

Product

Resources

About