Stock Price Prediction Using the ARIMA Model

Adebiyi, Ayodele Ariyo; Adediran, Adewumi; Ayo, C. K.

doi:10.1109/uksim.2014.67

Cited by 697 publications

(340 citation statements)

References 11 publications

Supporting

Mentioning

327

Contrasting

Unclassified

Order By: Relevance

“…Firstly, this study motivates by the more general evidence results that combined forecasting models obtain better forecasting results than the single model in Zhang study [16] investigated early a hybridization of ARIMA [17] and ANN models. In this combined method, the linear correlation assembly of the time series is demonstrated through ARIMA model, and remaining residuals, besides nonlinear part are modeled through ANN.…”

Section: Related Workmentioning

confidence: 92%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study is to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated, namely, ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model is examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

show abstract

Section: Related Workmentioning

confidence: 92%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…Thus, we employ two univariate time series models-ARIMA and NNAR. Application of ARIMA can be found in many fields of studies such as in finance (Ariyo et al 2014), shipping (Munim and Schramm 2017), logistics (Miller 2018), and electric power (Contreras et al 2003). Meanwhile, NNAR models are also used to forecast global solar radiation (Benmouiza and Cheknane 2013), river flow (Abrahart and See 2000), tourism demand (Álvarez-Díaz et al 2018).…”

Section: Forecast Methodsmentioning

confidence: 99%

Next-Day Bitcoin Price Forecast

Munim

Shakil

Alon

2019

JRFM

View full text Add to dashboard Cite

This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.

show abstract

“…Market prediction is particularly difficult due to its complex nature [4][5]. However, the dynamics are not entirely unpredictable.…”

Section: Introductionmentioning

confidence: 99%

“…The first set of inputs were several commonly available data namely the opening, closing, minimum and maximum daily past prices. The second set of inputs consist of several Moving Average (MA) over different intervals (5,10,20,50, 100, 200 days), a commonly used technical indicator for investors to estimate the direction of stock prices. These inputs were then used to train the MLP neural network to predict the next day prices for Bitcoin.…”

Section: Introductionmentioning

confidence: 99%