A Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid-19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) Model

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.

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ARIMA Model in Predicting Banking Stock Market Data

Modern Applied Science ◽

10.5539/mas.v12n11p309 ◽

2018 ◽

Vol 12 (11) ◽

pp. 309 ◽

Cited By ~ 3

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.

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Time Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive Model

Journal of Mathematics and Statistics ◽

10.3844/jmssp.2011.20.27 ◽

2011 ◽

Vol 7 (1) ◽

pp. 20-27 ◽

Cited By ~ 19

Author(s):

Suhartono

Keyword(s):

Time Series ◽

Moving Average ◽

Additive Model ◽

Time Series Forecasting ◽

Autoregressive Integrated Moving Average

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Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽

10.1371/journal.pone.0250149 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0250149

Author(s):

Fuad A. Awwad ◽

Moataz A. Mohamoud ◽

Mohamed R. Abonazel

Keyword(s):

Saudi Arabia ◽

Moving Average ◽

Arima Model ◽

Daily Basis ◽

World Health ◽

Arima Models ◽

Autoregressive Integrated Moving Average ◽

First Case ◽

Novel Coronavirus ◽

The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

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

International Research in Higher Education ◽

10.5430/irhe.v4n3p58 ◽

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.

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Early Detection of Covid-19 in Canadian Provinces and its Anticipatory Measures for a Medical Emergency

SN Computer Science ◽

10.1007/s42979-020-00347-0 ◽

2020 ◽

Vol 1 (6) ◽

Author(s):

Vikram Kumar Kamboj ◽

Chaman Verma ◽

Anish Gupta

Keyword(s):

Time Series ◽

Early Detection ◽

Moving Average ◽

Time Series Forecasting ◽

Medical Emergency ◽

Canadian Province ◽

Autoregressive Integrated Moving Average ◽

Entire World ◽

Canadian Provinces ◽

Day By Day

AbstractThe spread of COVID-19 is incearsing day by day and it has put the entire world and the whole humankind at the stack. The assets of probably the biggest economies are worried because of the enormous infectivity, and transmissibility of this ailment. Because of the developing extent of the number of cases and its ensuing weight on the organization and wellbeing experts, some expectation strategies would be required to anticipate the quantity of evidence in the future. In this paper, we have utilized time series forecasting approach entitled autoregressive integrated moving average, and bend fitting for the forecast of the quantity of COVID-19 cases in Canadian Province for 30 days ahead. The estimates of different parameters (number of positive cases, number of recouped cases, and decrease cases) got by the proposed strategy is exact inside a specific range, and will be a beneficial apparatus for overseers, and wellbeing officials to organize the clinical office in the distinctive Canadian Province.

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Using AutoRegressive Integrated Moving Average and Gaussian Processes with LSTM neural networks to predict discrete geomagnetic signals

10.5194/egusphere-egu2020-10385 ◽

2020 ◽

Author(s):

Laurentiu Asimopolos ◽

Alexandru Stanciu ◽

Natalia-Silvia Asimopolos ◽

Bogdan Balea ◽

Andreea Dinu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Moving Average ◽

Time Series Forecasting ◽

Series Data ◽

Autoregressive Integrated Moving Average ◽

Frequency Information ◽

Probabilistic Forecasts ◽

Geomagnetic Data ◽

Gp Model

In this paper, we present the results obtained for the geomagnetic data acquired at the Surlari Observatory, located about 30 Km North of Bucharest - Romania. The observatory database contains records from the last seven solar cycles, with different sampling rates.We used AR, MA, ARMA and ARIMA (AutoRegressive Integrated Moving Average) type models for time series forecasting and phenomenological extrapolation. ARIMA model is a generalization of an autoregressive moving average (ARMA) model, fitted to time series data to predict future points in the seriesWe made spectral analysis using Fourier Transform, that gives us a relevant picture of the frequency spectrum of the signal component, but without locating it in time, while the wavelet analysis provides us with information regarding the time of occurrence of these frequencies.&#160;Wavelet allows local analysis of magnetic field components through variable frequency windows. Windows with longer time intervals allow us to extract low-frequency information, medium-sized intervals of different sizes lead to medium-frequency information extraction, and very narrow windows highlight the high-frequencies or details of the analysed signals.We extend the study of geomagnetic data analysis and predictive modelling by implementing a Long Short-Term Memory (LSTM) recurrent neural network that is capable of modelling long-term dependencies and is suitable for time series forecasting. This method includes a Gaussian process (GP) model in order to obtain probabilistic forecasts based on the LSTM outputs.&#160;The evaluation of the proposed hybrid model is conducted using the Receiver Operating Characteristic (ROC) Curve that provides a probabilistic forecast of geomagnetic storm events.&#160;In addition, reliability diagrams are provided in order to support the analysis of the probabilistic forecasting models.The implementation of the solution for predicting certain geomagnetic parameters is implemented in the MATLAB language, using the Toolbox Deep Learning Toolbox, which provides a framework for the design and implementation of deep learning models.Also, in addition to using the MATLAB environment, the solution can be accessed, modified, or improved in the Jupyter Notebook computing environment.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.588-589.1466 ◽

2012 ◽

Vol 588-589 ◽

pp. 1466-1471 ◽

Cited By ~ 1

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.

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Modelling New Zealand Road Deaths

Journal of Road Safety ◽

10.33492/jrs-d-19-00246 ◽

2021 ◽

Vol 32 (2) ◽

pp. 4-15

Author(s):

Colin Morrison ◽

Ernest Albuquerque

Keyword(s):

Time Series ◽

New Zealand ◽

Moving Average ◽

Arima Model ◽

Time Series Forecasting ◽

Time Data ◽

Baseline Model ◽

Explanatory Variables ◽

Distributed Lag ◽

Autoregressive Distributed Lag

New Zealand is developing an integrated road safety intervention logic model. This paper describes a core component of this wider strategic research carried out in 2018: a baseline model that extrapolates New Zealand road deaths to 2025. The baseline will provide context to what Waka Kotahi NZ Transport Agency is trying to achieve. It offers a way of understanding what impact interventions have in acting with and against external influences affecting road deaths and serious trauma. The baseline model considers autonomous change at a macro level given social and economic factors that influence road deaths. Identifying and testing relationships and modelling these explanatory variables clarifies the effect of interventions. Time-series forecasting begins by carefully collecting and rigorously analysing sequences of discrete-time data, then developing an appropriate model to describe the inherent structure of the series. Successful time-series forecasting depends on fitting an appropriate model to the underlying time-series. Several time-series models were investigated in understanding road deaths in the New Zealand context. In the final modelling an autoregressive integrated moving average (ARIMA) model and two differing autoregressive distributed lag (ARDL) models were developed. A preferred model was identified. This ARDL model was used to project road deaths to 2025.

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Wavelet Transform and Autoregressive Integrated Moving Average Combination Approach in Crude Oil Prices Forecasting

International Journal of Innovative Computing ◽

10.11113/ijic.v8n2.177 ◽

2018 ◽

Vol 8 (2) ◽

Author(s):

Nurull Qurraisha Nadiyya Md-Khair ◽

Ruhaidah Samsudin ◽

Ani Shabri

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Crude Oil ◽

Moving Average ◽

Absolute Error ◽

Time Series Forecasting ◽

Autoregressive Integrated Moving Average ◽

Spot Prices ◽

Crude Oil Prices ◽

Combination Approach

This paper proposes a time series forecasting approach combining wavelet transform and autoregressive integrated moving average (ARIMA) to enhance the precision in forecasting crude oil spot prices series. Wavelet transform splits the original prices series into several subseries, then the most appropriate model of ARIMA is established to predict each respective series and finally all series are combined back to get the original series. The datasets for the experiment consist of crude oil spot prices from Brent North Sea (Brent) and West Texas Intermediate (WTI). Single forecasting model ARIMA and several existing forecasting approaches in the literatures are used to measure the performance of the proposed approach by utilizing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) collected. Final results have depicted that the proposed approach outperforms other approaches with smaller MAE and RMSE values. Ultimately, it is proven that data decomposition, combined with forecasting method can increase the accuracy in time series forecasting.

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