scholarly journals A Multiplicative Seasonal Arima Model for Nigerian Unemployment Rates

2012 ◽  
Vol 3 ◽  
pp. 46-53
Author(s):  
Ette Harrison Etuk
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Positive Trend ◽  
Seasonal Differences ◽  
Autoregressive Integrated Moving Average ◽  
Unemployment Rates ◽  
Seasonal Model ◽  
Time Plot

Time series analysis of Nigerian Unemployment Rates is done. The data used is monthly from 1948 to 2008. The time plot reveals a slightly positive trend with no clear seasonality. A multiplicative seasonal model is suggestive given seasonality that typically tends to increase with time. Seasonal differencing once produced a series with no trend nor discernible stationarity. A non-seasonal differencing of the seasonal differences yielded a series with no trend but with a correlogram revealing stationarity of order 12, a nonseasonal autoregressive component of order 3 and a seasonal moving average component of order 1. A multiplicative seasonal autoregressive integrated moving average (ARIMA) model, (3, 1, 0)x(0, 1, 1)12, is fitted to the series. It has been shown to be adequate.

scholarly journals Data Analysis Time Series For Forecasting The Greenhouse Effect

2019 ◽  
Vol 3 (2) ◽  
pp. 86
Author(s):  
Fauzah Umami ◽  
Hendra Cipta ◽  
Ismail Husein
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Greenhouse Effect ◽  
Reference Data ◽  
Moving Average ◽  
Arima Model ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
The Earth

<span lang="EN-US">The greenhouse effect is a term used to describe the earth having a greenhouse effect where the sun's heat is trapped by the earth's atmosphere. This study aims to model the greenhouse effect and then predict the greenhouse effect in the coming period using the Autoregressive Integrated Moving Average (ARIMA) method. In this case, time series analysis and reference data for 31 months are used, from the period January 2017 - July 2019, the results of the ARIMA model that are suitable for forecasting the greenhouse effect are ARIMA (4.2.0) with Mean Square Error (MSE) of 161885</span>

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

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.

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.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

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 Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

scholarly journals Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-Term Regional Temperature and Precipitation

2020 ◽  
Vol 35 (3) ◽  
pp. 959-976 ◽  
Author(s):  
Yuchuan Lai ◽  
David A. Dzombak
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Environmental Engineering ◽  
Statistical Techniques ◽  
Engineering Applications ◽  
Autoregressive Integrated Moving Average ◽  
Temperature And Precipitation ◽  
Regional Temperature ◽  
Near Term

Abstract A data-driven approach for obtaining near-term (2–20 years) regional temperature and precipitation projections utilizing local historical observations was established in this study to facilitate civil and environmental engineering applications. Given the unique characteristics of temporal correlation and skewness exhibited in individual time series of temperature and precipitation variables, a statistical time series forecasting technique was developed based on the autoregressive integrated moving average (ARIMA) model. Annual projections obtained from the ARIMA model—depending on individual series—can be interpreted as an integration of the most recent observations and the long-term historical trend. In addition to annual temperature and precipitation forecasts, methods of estimating confidence intervals for different return periods and simulating future daily temperature and precipitation were developed to extend the applicability for use in engineering. Quantitative comparisons of annual temperature and precipitation forecasts developed from the ARIMA model and other common statistical techniques such as a linear trend method were performed. Results suggested that while the ARIMA model cannot outperform all other techniques for all evaluated climate indices, the ARIMA model in general provides more accurate projections—especially interval forecasts—and is more reliable than other common statistical techniques. With the use of the ARIMA-based statistical forecasting model, interpretable and reliable near-term, location-specific temperature and precipitation forecasts can be obtained for consideration of changing climate in civil and environmental engineering applications.

scholarly journals A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

2020 ◽  
Vol 17 (19) ◽  
pp. 7162
Author(s):  
Xiaoxin Zhu ◽  
Yanyan Wang ◽  
David Regan ◽  
Baiqing Sun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Quantitative Study ◽  
Moving Average ◽  
Arima Model ◽  
Tohoku Earthquake ◽  
2011 Tohoku Earthquake ◽  
The 2011 Tohoku Earthquake ◽  
Food Commodity ◽  
Arima Processes

Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors.

scholarly journals COMPARISON BETWEEN HYBRID QUANTILE REGRESSION NEURAL NETWORK AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS VARIABLE FOR FORECASTING OF CURRENCY INFLOW AND OUTFLOW IN BANK INDONESIA

2018 ◽  
Vol 80 (6) ◽  
Author(s):  
Dedy Dwi Prastyo ◽  
Suhartono Suhartono ◽  
Agnes Ona Bliti Puka ◽  
Muhammad Hisyam Lee
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Quantile Regression ◽  
Empirical Result ◽  
Moving Average ◽  
Exogenous Variable ◽  
Real Case ◽  
Autoregressive Integrated Moving Average ◽  
Series Analysis

Some problems arise in time series analysis are nonlinearity and heteroscedasticity. Methods that can be used to analyze such problems are neural network and quantile regression. There are a lot of studies and developments on both methods, but the study that focuses on the performances of combination of these two methods applied in real case are still limited. Therefore, this study performed a comparison between hybrid Quantile Regression Neural Network (QRNN) and Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX). Both methods were employed to model the currency inflow and outflow from Bank Indonesia in Nusa Tenggara Timur province. Based on the empirical result, the hybrid QRNN method provided better forecasting for currency outflow whereas the ARIMAX resulted in better forecasting for the inflow. 

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