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Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
International Journal of Banking, Accounting and Finance, 2014The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-
Manish Kumar, M. Thenmozhi
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Proceedings of the Third International Workshop on Computational Transportation Science, 2010
Trajectory data play an important role in analyzing real world applications that involve movement features, e.g. natural and social phenomena such as bird migration, transportation management, urban planning and tourism analysis. Such trajectory data are a speical kind of time series with another focus on the spatial dimension besides the temporal one.
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Trajectory data play an important role in analyzing real world applications that involve movement features, e.g. natural and social phenomena such as bird migration, transportation management, urban planning and tourism analysis. Such trajectory data are a speical kind of time series with another focus on the spatial dimension besides the temporal one.
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2015
?? ???????????? ???????????????????????? ARIMA-???????????? ???????????????????? ?????????? ?? ???????????????????????? ?????????????? ???? ?????????????? ???????????????????? ????????????????????. ?????????????????? ???????????????????? ???????????? ?? ?????????????????????????????? ???????????????? ???????????????????? ???????? ?????????????????? ????
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?? ???????????? ???????????????????????? ARIMA-???????????? ???????????????????? ?????????? ?? ???????????????????????? ?????????????? ???? ?????????????? ???????????????????? ????????????????????. ?????????????????? ???????????????????? ???????????? ?? ?????????????????????????????? ???????????????? ???????????????????? ???????? ?????????????????? ????
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2017
The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes. Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes. MA and AR models can approximate mixed ARMA models.
Richard McCleary +2 more
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The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes. Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes. MA and AR models can approximate mixed ARMA models.
Richard McCleary +2 more
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2019
Chapter 2 introduces ARIMA algebra. With a few exceptions, this material mirrors the authors’ earlier work. The chapter begins with stationary time series processes – white noise, moving average (MA), and autoregressive (AR) processes – and moves predictably to non-stationary and multiplicative (seasonal) models. Stationarity implies
David McDowall +2 more
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Chapter 2 introduces ARIMA algebra. With a few exceptions, this material mirrors the authors’ earlier work. The chapter begins with stationary time series processes – white noise, moving average (MA), and autoregressive (AR) processes – and moves predictably to non-stationary and multiplicative (seasonal) models. Stationarity implies
David McDowall +2 more
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2021
This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
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This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
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Comparison of Drought Forecasting Using ARIMA and Empirical Wavelet Transform-ARIMA
2017Drought forecasting is important in preparing for drought and its mitigation plan. This paper focuses on the investigation of ARIMA and Empirical Wavelet Transform (EWT)-ARIMA in forecasting drought using Standard Precipitation Index (SPI). EWT is employed to decompose the time series into 4 modes. SPI of 3, 6, 9, 12 and 24 months were used.
Muhammad Akram bin Shaari +2 more
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2019
Published as part of Opitz, Weston, 2019, Descriptions of new genera and new species of Western Hemisphere checkered beetles (Coleoptera, Cleroidea, Cleridae), pp.
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Published as part of Opitz, Weston, 2019, Descriptions of new genera and new species of Western Hemisphere checkered beetles (Coleoptera, Cleroidea, Cleridae), pp.
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