1,720,968 research outputs found
A Compounded Multi-resolution-Artificial Neural Network Method for the Prediction of Time Series with Complex Dynamics
No full textBootstrap Order Determination for ARMA Models: A Comparison between Different Model Selection Criteria
Full text linkThe present paper deals with the order selection of models of the class for autoregressive moving average. A novel method—previously designed to enhance the selection capabilities of the Akaike Information Criterion and successfully tested—is now extended to the other three popular selectors commonly used by both theoretical statisticians and practitioners. They are the final prediction error, the Bayesian information criterion, and the Hannan-Quinn information criterion which are employed in conjunction with a semiparametric bootstrap scheme of the type sieve
Bootstrap Order Determination for ARMA Models: A Comparison between Different Model Selection Criteria
No full textThe present paper deals with the order selection of models of the class for autoregressive moving average. A novel method-previously designed to enhance the selection capabilities of the Akaike Information Criterion and successfully tested-is now extended to the other three popular selectors commonly used by both theoretical statisticians and practitioners. They are the final prediction error, the Bayesian information criterion, and the Hannan-Quinn information criterion which are employed in conjunction with a semiparametric bootstrap scheme of the type sieve
A Hybrid Computer-Intensive Approach Integrating Machine Learning and Statistical Methods for Fake News Detection
No full textIn this paper, we address the challenge of early fake news detection within the framework of anomaly detection for time-dependent data. Our proposed method is computationally intensive, leveraging a resampling scheme inspired by maximum entropy principles. It has a hybrid nature, combining a sophisticated machine learning algorithm augmented by a bootstrapped versions of binomial statistical tests. In the presented approach, the detection of fake news through the anomaly detection system entails identifying sudden deviations from the norm, indicative of significant, temporary shifts in the underlying data-generating process
A Rapid, Fully Automated Denoising Method for Time Series Utilizing Wavelet Theory
No full textA wavelet-based noise reduction method for time series is proposed. Traditional denoising techniques often adopt a “trial-and-error” approach, which can prove inefficient and may result in suboptimal filtering outcomes. In contrast, our method systematically selects the most suitable wavelet function from a predefined set, along with its associated tuning parameters, to ensure an optimal denoising process. The denoised series produced by this approach maximizes a suitable objective function based on information-theoretic divergence. This is particularly significant in economic time series, which are frequently characterized by non-linear dynamics and erratic patterns, often influenced by measurement errors and various external disturbances. The method’s performance is evaluated using time series data derived from the Business Confidence Climate Survey, which is freely and publicly accessible via the World Wide Web through the Italian National Institute of Statistics. The results of our empirical analysis demonstrate the effectiveness of the proposed method in delivering robust filtering capabilities, adeptly distinguishing informative signals from noise, and successfully eliminating uninformative components from the time series. This capability not only enhances the clarity of the data, but also significantly improves the overall reliability of subsequent analyses, such as forecasting
A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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