scholarly journals Predicting Closed Price Time Series Data Using ARIMA Model

2018 ◽  
Vol 12 (11) ◽  
pp. 181 ◽  
Author(s):  
S. AL Wadi ◽  
Mohammad Almasarweh ◽  
Ahmed Atallah Alsaraireh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Price Forecasting ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Short Term Prediction

Closed price forecasting plays a main rule in finance and economics which has encouraged the researchers to introduce a fit model in forecasting accuracy. The autoregressive integrated moving average (ARIMA) model has developed and implemented in many applications. Therefore, in this article the researchers utilize ARIMA model in predicting the closed time series data which have been collected from Amman Stock Exchange (ASE) from Jan. 2010 to Jan. 2018. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

scholarly journals Comparing of ARIMA and RBFNN for short-term forecasting

2015 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Haviluddin Haviluddin ◽  
Ahmad Jawahir
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Forecasting ◽  
Simulated Time ◽  
Modelling And Forecasting

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts.

scholarly journals Prediction Of Tiger Shrimp Supply Using Time Series Anlysis Method Case Study CV.Surya Perdana Benur

2021 ◽  
Vol 3 (1) ◽  
pp. 27-32
Author(s):  
Yoesril Ihza Mahendra ◽  
Natalia Damastuti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Economic Value ◽  
Series Data ◽  
Model Parameters ◽  
Marine Animal ◽  
Autoregressive Integrated Moving Average ◽  
Tiger Shrimp

Prediction of demand for tiger shrimp buyers using data from the company CV. Surya Perdana Benur. The process is carried out with the models in the Autoregressive Integrated Moving Average method. Tiger shrimp is a marine animal that is now widely cultivated by big company in Indonesia. Tiger shrimp has important economic value, so its existence must be maintained as part of Indonesian germplasm. The problem now faced by many tiger shrimp companies is the inadequate availability of goods for consumers. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. Autoregressive (AR), MovingAverage (MA), and Autoregressive Integrated Moving Average (ARIMA) model and will be evaluated through Mean Absolute Percent Error (MAPE). The initial process that will be carried out after the data is processed is model identification, estimation of model parameters, residual inspection, using forecasting models if the model has been fulfilled will be evaluated using MAPE until the results come out 14875.593875 to be able to predict the next buyer demand.

scholarly journals A New Period-Sequential Index Forecasting Algorithm for Time Series Data

2019 ◽  
Vol 9 (20) ◽  
pp. 4386 ◽  
Author(s):  
Hongyan Jiang ◽  
Dianjun Fang ◽  
Klaus Spicher ◽  
Feng Cheng ◽  
Boxing Li
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
New Period ◽  
Psi Method

A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets. It is also concluded that: There is a trend that the higher the correlation coefficient value of the reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (>0.4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.

Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 16 (8) ◽  
pp. 3519-3524
Author(s):  
Loh Chi Jiang ◽  
Preethi Subramanian
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Stock Index ◽  
Series Data ◽  
Autoregressive Integrated Moving Average

Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.

scholarly journals ARIMA Model in Predicting Banking Stock Market Data

2018 ◽  
Vol 12 (11) ◽  
pp. 309 ◽  
Author(s):  
Mohammad Almasarweh ◽  
S. AL Wadi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Short Term Prediction

Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jordan was selected as a tool to show the ability of ARIMA in forecasting banking data. Therefore, Daily data from 1993 until 2017 is used for this study. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

Sign in / Sign up

Export Citation Format

Share Document