scholarly journals Optimal Stochastic Forecast Models of Rainfall in South-West Region of Nigeria

2021 ◽  
Vol 7 (2) ◽  
pp. 1-20
Author(s):  
J. N. Onyeka-Ubaka ◽  
◽  
M. A . Halid ◽  
R. K Ogundeji
Keyword(s):  
Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Model Identification ◽  
Forecast Model ◽  
Absolute Error ◽  
Mean Square ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Ogun State

Rainfall estimates are important components of water resources applications, especially in agriculture, transport constructing irrigation and drainage systems. This paper aims to stochastically model and forecast the rainfall trend and pattern for a city, each purposively selected in five states of the South-Western Region of Nigeria. The data collected from Nigerian Meteorological Agency (NIMET) website are captured with fractional autoregressive integrated moving average (ARFIMA) and seasonal autoregressive integrated moving average (SARIMA) models. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are used for model identification, the models selected are subjected to diagnostic checks for the models adequacy. Several tests: Augmented Dickey Fuller (ADF), Ljung Box and Jarque Bera tests are used for investigating unit root, serial autocorrelation and normality of residuals, respectively; the mean square error, root mean square error and mean absolute error are employed in validating the optimal stochastic model for each city in all states, in which the model with the lowest error of forecasting of all competing models is suggested as the best. The analyses and findings suggest SARIMA(1,0,1)(1,1,0) [12], SARIMA(3,0,2)(1,0,0) [12], SARIMA(1,0,0)(1,1,0) [12], SARIMA(2,0,2)(2,1,0) [12] and SARIMA(0,0,1)(1,1,0) [12] for (Ibadan) Oyo State, (Ikorodu) Lagos State, (Osogbo) Osun State, (Abeokuta) Ogun State and (Akure) Ondo state, respectively. The seasonal ARIMA (SARIMA) model was proven to be the best optimal stochastic forecast model for forecasting rainfall in the selected cities. The SARIMA model was, therefore, recommended as a veritable technique that will assist decision makers (Government, Farmers, and Policymakers) to establish better strategies “aprior” on the management of rainfall against upcoming weather changes to ensure increase in agricultural yields for the betterment of the citizenry and general economic growth.

scholarly journals AZƏRBAYCANDA COVID-19 KORONAVİRUSUNA YOLUXMANIN PROQNOZLAŞDIRILMASI ÜÇÜN ARIMA MODELLƏRİNİN TƏTBİQİ

2021 ◽  
Vol 12 (1) ◽  
pp. 95-104
Author(s):  
Firəngiz Sadıyeva ◽  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

Məqalədə COVID-19 pandemiyasını proqnozlaşdırmaq üçün avtoreqressiv inteqrasiya edilmiş hərəkətli ortalama (ing. ARIMA. Autoregressive İntegrated Moving Average) modeli təklif edilmişdir. COVID-19 dünyada sürətlə yayılan və hazırda davam edən yeni növ pandemiyadır. Son dövrlərdə pandemiyaya yoluxanların sayı Azərbaycanda rekord həddə çatmışdır. Məhz bu səbəbdən COVID-19 pandemiyasının proqnozu məsələsinə baxılmışdır və bir neçə ayı əhatə edən real verilənlərlə eksperimentlərdə təklif edilmiş ARIMA modelinin COVID-19 zaman sıralarının proqnozlaşdırılması üçün müxtəlif parametrlərlə tətbiq edilmişdir. Verilənlər dedikdə, 22.01.2020 – 22.10.2020 tarixləri arasında Azərbaycan Respublikasının Səhiyyə Nazirliyi (www.sehiyye.gov.az) tərəfindən rəsmi olaraq qeydiyyata alınan gündəlik yoluxma hallarının sayı nəzərdə tutulur. Bu verilənlərdən istifadə etməklə, növbəti zaman aralığında ölkəmizdə baş verəcək yoluxma halları proqnoz edilmişdir. Bunun üçün ARIMA modelinə müxtəlif parametrlər verilmiş və uyğun olaraq hər bir modelin səhv dərəcəsi qiymətləndirilmişdir. Səhvin qiymətləndirilməsi üçün MAPE (Mean Absolute Persentace Error), MAE (Mean Absolute Error) və RMSE (Root Mean Square Error) funksiyaları istifadə edilib. Müqayisələr nəticəsində ən uyğun model seçilmişdir. Alınmış nəticələr ölkəmizdə pandemiya dövründə həm səhiyyə sistemi, həm də adi vətəndaşlar üçün vacib amildir. Əldə edilmiş nəticələr statistik metodların koronavirusa aid qeyri-stasionar zaman sıralarının proqnozlaşdırılmasının digər məsələlərə tətbiqində də məhsuldar ola biləcəyini təsdiqləyir.

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Agus Sulaiman ◽  
Asep Juarna
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

scholarly journals Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

2019 ◽  
Vol 7 (10) ◽  
pp. 894-909
Author(s):  
Rhuan Carlos Martins Ribeiro ◽  
Thaynara Araújo Quadros ◽  
John Jairo Saldarriaga Ausique ◽  
Otavio Andre Chase ◽  
Pedro Silvestre da Silva Campos ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Mean Square Error ◽  
High Performance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Mean Square

Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.

Predicting Rice Production using Autoregressive Integrated Moving Average Model

2019 ◽  
Vol 6 (04) ◽  
Author(s):  
R C BHARATI ◽  
ANIL KUMAR SINGH
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Time Series Data ◽  
Moving Average ◽  
Information Criterion ◽  
Rice Production ◽  
Series Data ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

A study was conducted on time-series data on rice production in India. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) time-series process was considered for predicting country's rice production using the time series data from 1950–51 to 2017–18. Data from 1950–51 to 2014–15 were used for model development and three years data from 2015–16 and 2017–18 were kept for validation The augmented Dicky Fuller test was applied to test stationarity in data set. Root mean square error. Based on ACF and PACF, the model was defined and tested for its suitability. Akaike information criterion and Bayesian information criterion were used to judge the suitability of the model to be fitted. The performance of the fitted model was examined using mean absolute error, mean percent forecast error, root mean square error and Theil's inequality coefficients. IMA (0, 1, 1) model performed well for forecasting purposes. The percent prediction error for the last three years i.e. from 2015–16 and 2017–18, was below 3%. The predicted values along with their standard errors up to the year 2099, were also obtained using the model.

scholarly journals PERAMALAN JUMLAH MAHASISWA MENGGUNAKAN MOVING AVERAGE

Telematika ◽  
2018 ◽  
Vol 15 (1) ◽  
pp. 67
Author(s):  
Hari Prapcoyo
Keyword(s):  
Mean Square Error ◽  
Mean Absolute Error ◽  
Average Length ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Square ◽  
Absolute Percentage Error ◽  
Weighted Moving Average

AbstractThe Process of using resources in higher education is influenced by the up and down of the number students. The purpose of this study is to predict the number of students who study in the department of informatics engineering UPN Veteran Yogyakarta for the next periods. This research, data is taken from forlap dikti for Informatics Engineering fom 2009 until 2016 at UPN Veteran Yogyakarta. The method that used to forecast the number of students is a Moving Average method consisting of: Single Moving Average (SMA), Weighted Moving Average (WMA) and Exponential Moving Average (EMA). This study will use the forecasting accuracy namely Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to select the best model to be used for forecasting. The best model that used for forecasting is Weighted Moving Average (WMA) with weighted 1/3 and average length (n) used for 2. The smallest value for MSE of 5807.96; the smallest MAE value of 55.89 and the smallest value for MAPE of 5.24%. Forecasting of the number of students for four semesters in the future after the even semester of 2016 are respectively: 902; 901,33; 901,56 and 901,48. Keywords : Forecasting, UPN Veteran Yogyakarta, Single moving average(SMA) AbstrakProses penggunaan sumber daya perguruan tinggi setiap tahun dipengaruhi oleh naik turunnya jumlah mahasiswa. Tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa yang kuliah di jurusan teknik informatika UPN Veteran Yogyakarta untuk periode yang akan datang. Data penelitian ini diambil dari forlap dikti untuk Teknik Informatika dari tahun 2009 sampai 2016 UPN Veteran Yogyakarta. Metode yang digunakan untuk melakukan peramalan jumlah mahasiswa adalah metode Moving Average yang tediri dari : Single Moving Average (SMA), Weighted Moving Average (WMA) dan Exponential Moving Average (EMA). Penelitian ini akan menggunkan akurasi peramalan Mean Square Error (MSE), Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE) untuk memilih model terbaik yang akan digunakan untuk peramalan. Model terbaik yang digunakan untuk peramalan yaitu Weighted Moving Average (WMA) dengan pembobot 1/3 dan panjang rata-rata (n) yang dipakai sebesar 2. Nilai terkecil untuk MSE sebesar 5807,96; nilai terkecil MAE sebesar 55,89 dan nilai terkecil untuk MAPE sebesar 5,24 %. Peramalan untuk jumlah mahasiswa empat semester kedepan setelah semester genap 2016 masing-masing adalah : 902; 901,33; 901,56 dan 901,48. Kata Kunci : Peramalan, UPN Veteran Yogyakarta, Single Moving Average(SMA).

scholarly journals Stochastic ARIMA model for annual rainfall and maximum temperature forecasting over Tordzie watershed in Ghana

2018 ◽  
Vol 37 (1) ◽  
pp. 127-140 ◽  
Author(s):  
Mexoese Nyatuame ◽  
Sampson K. Agodzo
Keyword(s):  
Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Complex Nature ◽  
Mean Square ◽  
Rainfall Prediction

AbstractThe forecast of rainfall and temperature is a difficult task due to their variability in time and space and also the inability to access all the parameters influencing rainfall of a region or locality. Their forecast is of relevance to agriculture and watershed management, which significantly contribute to the economy. Rainfall prediction requires mathematical modelling and simulation because of its extremely irregular and complex nature. Autoregressive integrated moving average (ARIMA) model was used to analyse annual rainfall and maximum temperature over Tordzie watershed and the forecast. Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to identify the models by aid of visual inspection. Stationarity tests were conducted using the augmented Dickey–Fuller (ADF), Mann–Kendall (MK) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests respectively. The chosen models were evaluated and validated using the Akaike information criterion corrected (AICC) and also Schwartz Bayesian criteria (SBC). The diagnostic analysis of the models comprised of the independence, normality, homoscedascity, P–P and Q–Q plots of the residuals respectively. The best ARIMA model for rainfall for Kpetoe and Tordzinu were (3, 0, 3) and (3, 1, 3) with AICC values of 190.07 and 178.23. That of maximum temperature for Kpetoe and Tordzinu were (3, 1, 3) and (3, 1, 3) and the corresponding AICC values of 23.81 and 36.10. The models efficiency was checked using sum of square error (SSE), mean square error (MSE), mean absolute percent error (MAPE) and root mean square error (RMSE) respectively. The results of the various analysis indicated that the models were adequate and can aid future water planning projections.

scholarly journals Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.

2020 ◽  
Author(s):  
Sudhir Bhandari ◽  
Amit Tak ◽  
Jitendra Gupta ◽  
Bhoopendra Patel ◽  
Jyotsna Shukla ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Evidence Based ◽  
Mean Square ◽  
Time Data ◽  
Autoregressive Integrated Moving Average ◽  
State Of Affairs

Abstract The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.

scholarly journals Forecasting Exports and Imports by using Autoregressive (AR) with Seasonal Dummies and Box-Jenkins Approaches: A Case of Pakistan

2020 ◽  
Vol 31 (3) ◽  
pp. 291-301
Author(s):  
Sahir Pervaiz Ghauri ◽  
Rizwan Raheem Ahmed ◽  
Dalia Streimikiene ◽  
Justas Streimikis
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Trade Deficit ◽  
Absolute Error ◽  
Ar Model ◽  
Mean Square ◽  
Auto Regressive

This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87% for the FY2020 and projected forecasting for the import demonstrates a negative variation of -1.61% for the FY2020. The findings of the undertaken study recommend the policymakers of Pakistan to take corrective measures to increase exports and to prevent the country from the trade deficit. The policymakers of Pakistan should give more incentives to the exporters and decrease the cost of doing business to be more competitive than the regional economies such as India, Bangladesh, and China.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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