Forecasting Crude Oil Price with an Autoregressive Integrated Moving Average (ARIMA) Model

Zhao, Chen; Wang, Bing

doi:10.1007/978-3-642-38667-1_27

Cited by 24 publications

(17 citation statements)

References 12 publications

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“…Forecasting methods include Autoregressive Integrated Moving Average (ARIMA) models [64,65], Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models [66][67][68][69], and Vector Auto-Regression (VAR) models [70,71]. Also, artificial intelligence methods such as Support Vector Machine (SVM) [72] and Artificial Neural Networks (ANNs) [73] are used.…”

Section: Forecasting Methodsmentioning

confidence: 99%

Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

2019

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The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions.

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Section: Forecasting Methodsmentioning

confidence: 99%

Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

2019

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“…Peramalan dengan model ARIMA memudahkan dan memberikan kepuasan dalam menghasilkan peramalan (Ho & Xie, 1998). Peramalan dengan arima model merupakan peramalan short-term forecast (Zhao & Wang, 2014). Pada penelitian Hatidja melakukan peramalan pada perusahaan telekomunikasi PT.…”

Section: Pendahuluanunclassified

Peramalan dan deteksi outlier saham perusahaan angkutan laut umum di masa covid-19 dengan pemodelan arima

Thaib

Thabrani

Netsyah

2021

JKMW

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The public sea freight sector is one of the affected by COVID-19. PT. Samudera Indonesia Tbk is one of the sea transportations companies in Indonesia. The ARIMA model in the previous study provided a statistical test with the aim of evaluating the suitability of the model with a p value of less than 0.05 to determine ARIMA by guessing through ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) through stationary data. Outlier detection can be done by plotting the residuals from the specified model. Forecasting data for the next 5 days using the ARIMA (3,1,2) model can be seen that the results of forecasting stock price data for PT. Samudera Indonesia Tbk using ARIMA (3,1,2) is within the 95% confidence interval with a forecast value that is close to the actual value. There are outliers that are detected which are related to economic phenomena.Keywords: Forecasting, Covid-19, stock, ARIMA, outlier

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“…The results demonstrate good prediction effects. A similar study by Zhao and Wang (2014) uses ARIMA modelling to predict the average annual price of world crude oil. The findings reveal that the model has the perfect approach capability and predictive efficiency for short-term forecasting.…”

Section: Econometric Approachesmentioning

confidence: 99%

A novel hybrid approach to forecast crude oil futures using intraday data

Manickavasagam

Visalakshmi

Apergis

2020

Technological Forecasting and Social Change

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Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This study presents two novel hybrid models to forecast WTI and Brent crude oil prices using combinations of machine learning and nature inspired algorithms. The first approach, MARSplines-IPSO-BPNN, Multivariate Adaptive Regression Splines (MARSPlines) find the important variables that affect crude oil prices. Then, the selected variables are fed into an Improved Particle Swarm Optimization (IPSO) method to obtain the best estimates of the parameters of the Backpropagation Neural Network (BPNN). Once these parameters are obtained, the variables are fed into the BPNN model to generate the required forecasts. The second approach, MARSplines-FPA-BPNN, generates the parameters of BPNN through the Flower Pollination Algorithm (FPA). The forecasting ability of these new models is compared to certain benchmark models. The findings document that the MARSplines-FPA-BPNN model performs better than the other competitive models.

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Forecasting Crude Oil Price with an Autoregressive Integrated Moving Average (ARIMA) Model

Cited by 24 publications

References 12 publications

Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development

Peramalan dan deteksi outlier saham perusahaan angkutan laut umum di masa covid-19 dengan pemodelan arima

A novel hybrid approach to forecast crude oil futures using intraday data

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