Results 81 to 90 of about 21,636,733 (224)
پژوهشهای ریاضی, 2020 Introduction The data obtained from observing a phenomenon over time is very common. One of the most popular models in time series and signal processing is the Autoregressive moving average model (ARMA).
Mahmod Afshari +2 more
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Malaysian Journal of Computing, 2020 The main purpose of this study is to compare the performances of univariate and bivariate models on four-time series variables of the crude palm oil industry in Peninsular Malaysia.
Pauline Jin Wee Mah, Nur Nadhirah Nanyan
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A Generalized ARFIMA Process with Markov-Switching Fractional Differencing Parameter [PDF]
We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the
Wen-Jen Tsay, Wolfgang Härdle
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IET Intelligent Transport Systems, Volume 18, Issue 4, Page 691-708, April 2024.Based on multi‐source data, this study couples the travel characteristics identifying by introducing a concept of service dependency degree and a Bayesian optimization–long short time memory–convolutional neural network method to conduct the multi‐task online car‐hailing demand prediction. This method is applied to the main scenic spots in Beijing, and
Zile Liu +3 more
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"Realized Volatility Risk" [PDF]
In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in ...
David E. Allen +2 more
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"Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model" [PDF]
In this paper, we apply the ARFIMA-GARCH model to the realized volatility and the continuous sample path variations constructed from high-frequency Nikkei 225 data.
Isao Ishida, Toshiaki Watanabe
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Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach [PDF]
Previous models of monthly CPI inflation time series have focused on possible regime shifts, non-linearities and the feature of long memory. This paper proposes a new time series model, named Adaptive ARFIMA; which appears well suited to describe ...
Claudio Morana, Richard T. Baille
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Optimal spectral bandwidth for long memory [PDF]
, 1993 For long range dependent time series with a spectral singularity at frequency zero, a theory for optimal bandwidth choice in non-parametric analysis ofthe singularity was developed by Robinson (1991b).
Delgado, Miguel A., Robinson, Peter M.
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WIREs Data Mining and Knowledge Discovery, Volume 14, Issue 1, January/February 2024.A review of recent research on the application of deep learning models to price forecast of financial time series, with information on model architectures, applications, advantages and disadvantages, and directions for future research. Abstract Accurately predicting the prices of financial time series is essential and challenging for the financial ...
Cheng Zhang +2 more
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Motricidade, 2013 This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different components of HRV memory (short and ...
Argentina Leite +2 more
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