ARIMA Model in Predicting Banking Stock Market Data

Almasarweh, Mohammad Salameh; Wadi, S. Al

doi:10.5539/mas.v12n11p309

Cited by 65 publications

(39 citation statements)

References 12 publications

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“…A time series

follows the auto regressive-moving average (ARMA) model if :

where

and

prefers to auto-regressive part (AR), moving average part (MA), and white noise respectively [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…The auto-regressive integrated moving average (ARIMA) models are an extension of the ARMA models which is presented by the symbol ARIMA(p, d, q) and it is expressed as follows :

where

denote orders of auto-regression, q is the order of moving average and d is the number of differencing times. If

0

then the ARIMA model becomes to ARMA model [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Talkhi

Fatemi

Amirghofran

et al. 2021

Biomedical Signal Processing and Control

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Background The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. Methods In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. Results Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2,484 new confirmed and 114 new death cases of COVID-19. Conclusion According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

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“…A time series

follows the auto regressive-moving average (ARMA) model if :

where

and

prefers to auto-regressive part (AR), moving average part (MA), and white noise respectively [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…The auto-regressive integrated moving average (ARIMA) models are an extension of the ARMA models which is presented by the symbol ARIMA(p, d, q) and it is expressed as follows :

where

denote orders of auto-regression, q is the order of moving average and d is the number of differencing times. If

0

then the ARIMA model becomes to ARMA model [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Talkhi

Fatemi

Amirghofran

et al. 2021

Biomedical Signal Processing and Control

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show abstract

“…Wadi et al [1] found the best-fit ARIMA model for forecasting the closed price of Jordan's ASE, with p, d, and q parameters of 2, 1, and 1 correspondingly, and RMSE=4 . For the banking stock data of Jordan's ASE, the ARIMA p=1, d=1, and q=2 suited most with RMSE=1.4 Almasarweh and Alwadi [2]. While forecasting the trucking prices in the US, Miller [3] found that the ARIMA with p=1, d=1, and q=0 best fitted to forecast the Truckload.BLS data.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting non-linear WPI of manufacture of chemicals and chemical products in India: an MLP approach

Das¹,

Chakrabarti²

2021

IJATEE

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Forecasting predicts/ estimates the future by taking into account historical data. It's an instrument of decision-making that assists companies/ enterprises/ institutions in managing uncertainties. It allows the business to set up goals and create a budget. Forecasting helps us to anticipate changes and therefore guide us towards data-driven strategies and reasoned decisions/ choices. It serves as a proactive rather than a reactive measure. There are several forecasting approaches for the univariate time series (TS) data, e.g. *Author for correspondence Auto-Regressive Integrated Moving Average (ARIMA) [1-3], exponential smoothing (ES) [4-6], Support Vector Regression (SVR) [7, 8, 9], neural approaches [10-12]. The recent studies show that the researchers employed these approaches for forecasting various TS data, e.g., stock prices [1, 2, 13], prices of agricultural products [4, 5, 6], price index [10, 14].India's chemical industry is highly diverse, covers a wide range of products, and has a large import and export base. More than two million people are working here. On the economic growth of India, this sector's contribution is notable. In the future, there exists a significant opportunity for the industry's business/ revenue growth.

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“…Parametric models, such as Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroskedasticity (ARCH), have been used in various financial sectors. In particular, both the work of [5] and [6] applied ARIMA model in making prediction on banking stock market data and Chinese manufacturing industry. Yunus et al [7] use ARIMA model to capture time correlation and offer possibility distribution of collection records for determined wind-pace time.…”

Section: A Machine Learning Approachesmentioning

confidence: 99%

Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression

Zhou

Song

et al. 2020

IEEE Access

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It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods. INDEX TERMS Long short-term memory, convolutional neural network, attention mechanism, financial data analysis, deep learning.

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ARIMA Model in Predicting Banking Stock Market Data

Cited by 65 publications

References 12 publications

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Forecasting non-linear WPI of manufacture of chemicals and chemical products in India: an MLP approach

Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression

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