scholarly journals Forecasting GDP Movements in Nepal Using Autoregressive Integrated Moving Average (ARIMA) Modelling Process

2019 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Surya Bahadur Rana
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
Sample Data ◽  
Moving Average Model ◽  
Annual Time Series ◽  
Annual Time ◽  
Modelling Process

This study attempts to test the ARIMA model and forecast annual time series of GDP in Nepal from mid-July, 1960 to mid-July, 2018. The annual time series on GDP used in this study consists of total 59 observations. Out of them, three years’ data from mid-July 2016 to mid-July 2018 have been used for in-sample forecasting and evaluation. The study uses univariate Box-Jenkins ARIMA modelling process to identify the best fitted model that describes the sample data set. The study examines a number of ARIMA family models and recommends ARIMA (0,1,2) as the most appropriate model that best describes the annual GDP series of the sampled period. The ARIMA (0, 1, 2) model incorporates zero lag order for autoregression, integrated with 2 lag order for moving average model using first difference operator. The ARIMA model forecasts documented in this study are not significantly different from actual because the actual annual GDP series observed in forecast period fall within 95 per cent confidence interval of estimates. Hence, ARIMA (0,1,2) model can best capture the GDP movement in Nepal for the sample period.

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.

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 Determining the Pension Benefit Obligation of a Defined Benefit Plan: Applying a Multivariate ARIMA Stochastic Model

2022 ◽  
Vol 17 (4) ◽  
pp. 145
Author(s):  
Jeffrey Tim Query ◽  
Evaristo Diz
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Pension Benefit ◽  
Defined Benefit ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
Statistical Estimates ◽  
Moving Average Model ◽  
Series Type ◽  
Sample Time

<p>In this study we examine the robustness of fit for a multivariate and an autoregressive integrated moving average model to a data sample time series type.  The sample is a recurrent actuarial data set for a 10-year horizon.  We utilize this methodology to contrast with stochastic models to make projections beyond the data horizon. Our key results suggest that both types of models are useful for making predictions of actuarial liability levels given by PBO Projected Benefit Obligations on and off the horizon of the sample time series.  As we have seen in prior research, the use of multivariate models for control and auditing purposes is widely recommended.  Fast and reliable statistical estimates are desirable in all cases, whether for audit purposes or to verify and validate miscellaneous actuarial results.</p>

scholarly journals Forecasting Zambia’s Gross Domestic Product Using Time Series Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
Vol 5 (9) ◽  
pp. 440-447
Author(s):  
Chikumbe Evans Sankwa ◽  
Sikota Sharper
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Moving Average ◽  
Social Indicators ◽  
Arima Model ◽  
Information Criteria ◽  
Domestic Product ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
Study Results

Gross Domestic Product is one of the social indicators of development. This study attempts to model Zambia’s Gross domestic product using the Autoregressive Integrated Moving Average (ARIMA) model. This model has proved to help many countries during economic recession or when there is any disruption in the economic system due to pandemics or natural disasters. The study utilized a time series dataset from 1960 to 2018. The best model that fit the data set, following the selection model criteria, was ARIMA (5,2,0) model with the lowest Akaike’s Information Criteria(AIC) and Bayesian Information Criteria (BIC) and smallest volatility. The study results showed that, on average, Zambia’s gross domestic product will continue to rise over the next eight years. However, few recession (decline) points are expected in the period 2020 to 2022. It is hoped that the forecasts would be useful for researchers in Zambia, including the fiscal and monetary policy makers.

scholarly journals Forecasting the GDP per Capita for Egypt and Saudi Arabia Using ARIMA Models

2020 ◽  
Vol 11 (1) ◽  
pp. 247
Author(s):  
Noura Eissa
Keyword(s):  
Saudi Arabia ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Gdp Per Capita ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Annual Time Series ◽  
Per Capita ◽  
Annual Time

Annual time series data is used to forecast GDP per capita using the Box-Jenkins Autoregressive-Integrated Moving-Average (ARIMA) model for the Egyptian and Saudi Arabian economies. The fitted ARIMA model is tested for per capita GDP forecasting of Egypt and of Saudi Arabia for the next ten years. Conclusions convey that the most accurate statistical model as in previous literature that forecast GDP per capita for Egypt and for Saudi Arabia is ARIMA (1,1,2) and ARIMA (1,1,1) respectively. The diagnostic tests reveal that the two models presented individually are both stable and reliable.

scholarly journals Forecasting and predicting intussusception in children younger than 48 months in Suzhou using a seasonal autoregressive integrated moving average model

BMJ Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. e024712
Author(s):  
Wan-liang Guo ◽  
Jia Geng ◽  
Yang Zhan ◽  
Ya-lan Tan ◽  
Zhang-chun Hu ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Early Warning Systems ◽  
Early Summer ◽  
Warning Systems ◽  
Epidemiological Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Treatment And Prevention ◽  
Moving Average Model ◽  
Modelling Process

ObjectiveThe aims of this study were to highlight some epidemiological aspects of intussusception cases younger than 48 months and to develop a forecasting model for the occurrence of intussusception in children younger than 48 months in Suzhou.DesignA retrospective study of intussusception cases that occurred between January 2007 and December 2017.SettingRetrospective chart reviews of intussusception paediatric patients in a large Children’s hospital in South-East China were performed.ParticipantsThe hospital records of 13 887 intussusception cases in patients younger than 48 months were included in this study.InterventionsThe modelling process was conducted using the appropriate module in SPSS V.23.0.MethodsThe Box-Jenkins approach was used to fit a seasonal autoregressive integrated moving average (ARIMA) model to the monthly recorded intussusception cases in patients younger than 48 months in Suzhou from 2007 to 2016.ResultsEpidemiological analysis revealed that intussusception younger than 48 months was reported continuously throughout the year, with peaks in the late spring and early summer months. The most affected age group was younger than 36 months. The time-series analysis showed that an ARIMA (1,0,1 1,1,1)12model offered the best fit for surveillance data of intussusception younger than 48 months. This model was used to predict intussusception younger than 48 months for the year 2017, and the fitted data showed considerable agreement with the actual data.ConclusionARIMA models are useful for monitoring intussusception in patients younger than 48 months and provide an estimate of the variability to be expected in future cases in Suzhou. The models are helpful for predicting intussusception cases in Suzhou and could be useful for developing early warning systems. They may also play a key role in early detection, timely treatment and prevention of serious complications in cases of intussusception.

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

Sign in / Sign up

Export Citation Format

Share Document