SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction. [PDF]
PeerJ, 2023 Yan W +5 more
europepmc +1 more source
Stock market volatility simulation with the LSTM neural network
Вестник Пермского университета: Серия ЭкономикаIntroduction. Stock market volatility simulation and forecast are relevant issues which could contribute into lower risks and higher revenues of the market transactions.
Dmitry Aleksandrovich Patlasov +1 more
doaj +1 more source
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
core
South African inflation modelling using bootstrapped long short-term memory methods. [PDF]
SN Bus Econ, 2023 Kubheka S.
europepmc +1 more source
Assessing the Impact of Market Microstructure Noise and Random Jumps on the Relative Forecasting Performance of Option-Implied and Returns-Based Volatility [PDF]
This paper presents a comprehensive empirical evaluation of option-implied and returns-based forecasts of volatility, in which new developments related to the impact on measured volatility of market microstructure noise and random jumps are explicitly ...
Andrew Reidy +2 more
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Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models [PDF]
We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory ...
Kaizoji, Taisei, Lux, Thomas
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Time Analysis of an Emergent Infection Spread Among Healthcare Workers: Lessons Learned from Early Wave of SARS-CoV-2. [PDF]
Int J Gen Med, 2022 Leme PAF +8 more
europepmc +1 more source
A note on Michelacci and Zaffaroni, long memory, and time series of economic growth [PDF]
long memory, economic ...
B. Verspagen, G. Silverberg
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Predicción mediante modelos AFIRMA y FOU de energía afluente
Memoria Investigaciones en Ingeniería, 2017 En este trabajo se estudian predicciones a partir de modelos ARFIMA y FOU para la serie de datos semanales de energía afluente generada por las represas hidroeléctricas de Uruguay entre 1909 y 2012. Se describe la serie de datos, y mediante la estimación
Juan Kalemkerian
doaj
Model-based stationarity filtering of long-term memory data applied to resting-state blood-oxygen-level-dependent signal. [PDF]
PLoS One, 2022 Bansal IR +4 more
europepmc +1 more source