Trend Analysis in Sugarcane Growth in Mumias Sugar Belt, Western Kenya; for the Period 1985-2015

The study was carried out to examine trends in the output and acreage in the Mumias Sugar belt from the period 1985-2015. We used secondary data collected from Mumais Sugar Company records for the period 1985-2015 for the study. The trend analysis of sugarcane production in the Mumias Sugar Belt is important, where sugarcane is the major cash crop and absorbs a majority of the agrarian population in the region. The study used the expert modeler, an autoregressive integrated moving average (ARIMA), to predict the output. The forecast period was 2016 through March 2021 and employed two scenarios: I) forecast with +2 harvesting age predictor modification and ii) forecast with +10 hectares predictor modification. The predicted value showed good agreement with the observed values from the series plot, indicating that the model has a good predictive ability. The application of the model revealed that the results in the prediction tables show that, in each of the six forecasted quarters, increasing the harvesting age by two months is expected to generate about 4.52 more tons of yields per hectare than increasing area harvested by 10 hectares that would decrease the yield by 0.01 tons per hectare. The study recommends research and development on sugarcane varieties that mature early, making sugarcane-based Agri- enterprises and sustainable. In addition, Mumias Sugar Company should seek profitable techniques to increase the recovery per cent, and farmers seek good management practices to increase the efficiency of the sugarcane farms in the sugar belt.

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PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average)

JURNAL ILMIAH SAINS ◽

10.35799/jis.14.2.2014.5927 ◽

2014 ◽

Vol 14 (2) ◽

pp. 60

Author(s):

Greis S Lilipaly ◽

Djoni Hatidja ◽

John S Kekenusa

Keyword(s):

Stock Price ◽

Moving Average ◽

Secondary Data ◽

Research Use ◽

Autoregressive Integrated Moving Average ◽

Minimum Data ◽

Research Show

PREDIKSI HARGA SAHAM PT. BRI, Tbk. MENGGUNAKAN METODE ARIMA (Autoregressive Integrated Moving Average) Greis S. Lilipaly1) , Djoni Hatidja1) , John S. Kekenusa1) ABSTRAK Metode ARIMA adalah salah satu metode yang dapat digunakan dalam memprediksi perubahan harga saham. Tujuan dari penelitian ini adalah untuk membuat model ARIMA dan memprediksi harga saham PT. BRI, Tbk. bulan November 2014. Penelitian menggunakan data harga saham harian maksimum dan minimum PT. BRI, Tbk. Data yang digunakan yaitu data sekunder yang diambil dari website perusahaan PT. BRI, Tbk. sejak 3 Januari 2011 sampai 20 Oktober 2014 untuk memprediksi harga saham bulan November 2014. Dari hasil penelitian menunjukkan bahwa data tahun 2011 sampai Oktober 2014 bisa digunakan untuk memprediksi harga saham bulan November 2014. Hasilnya model ARIMA untuk harga saham maksimum adalah ARIMA (2,1,3) dan harga saham minimum adalah model (2,1,3) yang dapat digunakan untuk memprediksi data bulan November 2014 dengan validasi prediksi yang diambil pada bulan Oktober 2014 untuk selanjutnya dilakukan prediksi bulan November 2014. Kata Kunci: Metode ARIMA, PT. BRI, Tbk., Saham THE PREDICTION STOCK PRICE OF PT. BRI, Tbk. USE ARIMA METHOD (Autoregressive Integrated Moving Average) ABSTRACT ARIMA method is one of the method that used to prediction the change of stock price. The purpose of this research is to make model of ARIMA and predict stock price of PT. BRI, Tbk. in November 2014. The research use maximum and minimum data of stock price daily of PT. BRI, Tbk. Data are used is secondary data that taking from website of PT. BRI, Tbk. since January 3rd 2011 until October 20th 2014 to predict stock price in November 2014. From this research show that data from 2011 until October 2014 can be used to predict the stock price in November 2014. The result of ARIMA’s model for the maximum stock price is ARIMA (2,1,3) and the minimum stock price is (2,1,3) can use to predict the data on November 2014 with predict validation that take on October 2014 and with that predict November 2014. Keywords: ARIMA method, PT. BRI, Tbk., Stock

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Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.775114 ◽

2021 ◽

Vol 8 ◽

Author(s):

Veerasak Punyapornwithaya ◽

Katechan Jampachaisri ◽

Kunnanut Klaharn ◽

Chalutwan Sansamur

Keyword(s):

Milk Production ◽

Hybrid Model ◽

Moving Average ◽

Seasonal Fluctuation ◽

Predictive Ability ◽

Northern Region ◽

Average Error ◽

Autoregressive Integrated Moving Average ◽

Time Series Forecast ◽

Forecast Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

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Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model

E3S Web of Conferences ◽

10.1051/e3sconf/20187312010 ◽

2018 ◽

Vol 73 ◽

pp. 12010 ◽

Cited By ~ 6

Author(s):

Yenni P. Pasaribu ◽

Hariani Fitrianti ◽

Dessy Rizki Suryani

Keyword(s):

Global Climate ◽

Human Life ◽

Moving Average ◽

Arima Model ◽

Secondary Data ◽

Crop Failure ◽

Maximum Rainfall ◽

Autoregressive Integrated Moving Average ◽

Agriculture Sector ◽

Rainfall Forecast

Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.

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FORECASTING ARRIVAL OF FOREIGN TOURISTS USING SEASONAL ARIMA BOX-JENKINS

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol15iss2pp223-230 ◽

2021 ◽

Vol 15 (2) ◽

pp. 223-230

Author(s):

Nur Ilmayasinta

Keyword(s):

Moving Average ◽

Seasonal Pattern ◽

Secondary Data ◽

Autoregressive Integrated Moving Average ◽

Tourism Sector ◽

Existing Data

Indonesia's economy is influenced by many factors, including the tourism sector. Through this tourism sector, it is possible for many foreign tourists to visit Indonesia. There are so many foreign tourists who come to Indonesia, forecasting is needed to find out the estimates of foreign tourists in the following months based on existing data. The method that used is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The foreign tourist’s coming to Indonesia through Soekarno Hatta Airport were taken from the center agency on statistics (BPS) Indonesia. Data on the number of foreign tourists who come to Indonesia through Soekarno Hatta Airport is data with a seasonal pattern. The data used is secondary data obtained from Soekarno Hatta Airport for the period January 2010 to June 2015. In this case it is used to predict the value of the data for the next 6 months using the best model is the . Forecasting results show the number of each month increases from the previous year. In July it showed the highest yield of 342536, which was 297878 in the previous year. Forecasting results show the number of each month increases from the previous year. In July, the highest yield was 342536, which was 297878 in the previous year.

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Trend Analysis and Forecast of Precipitation, Reference Evapotranspiration, and Rainfall Deficit in the Blackland Prairie of Eastern Mississippi

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0265.1 ◽

2016 ◽

Vol 55 (7) ◽

pp. 1425-1439 ◽

Cited By ~ 25

Author(s):

Gary Feng ◽

Stacy Cobb ◽

Zaid Abdo ◽

Daniel K. Fisher ◽

Ying Ouyang ◽

...

Keyword(s):

Trend Analysis ◽

Management Practices ◽

Reference Evapotranspiration ◽

Cropping Systems ◽

Moving Average ◽

Cropping System ◽

Absolute Error ◽

Annual Rainfall ◽

Rainfall Deficit ◽

Dryness Index

AbstractTrend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ETo, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ETo, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894–2014) were analyzed for annual, seasonal, and monthly periods. The analysis showed historical average annual rainfall, ETo, and dryness index (DI) in the location to be 1307 mm, 1210 mm, and 0.97, respectively. Monthly rainfall and ETo ranged from 72 to 118 mm and from 94 to 146 mm, respectively, between May and October, resulting in a monthly rain deficit from 22 to 62 mm. Annual rainfall showed an increasing trend of 1.17 mm yr−1 while annual ETo exhibited a decreasing trend of −0.51 mm yr−1, resulting in an annual DI reduction of 0.001 per year. Seasonal trends were found for rainfall in autumn (1.06 mm yr−1), ETo in summer (−0.29 mm yr−1) and autumn (−0.18 mm yr−1), and DI in autumn (−0.006). An autoregressive, integrated, and moving-average (ARIMA) approach was used to model monthly and annual rainfall, ETo, and DI and to predict those values in the future. Low values of the root-mean-square error (RMSE) and mean absolute error (with both statistics being normalized to the mean of the observed values), low values of average percent bias, and low values of the ratio of the RMSE to the standard deviation of observed data, along with values of 1.0 for Nash–Sutcliffe modeling efficiency and the index of agreement, all suggest that the performance of the models is acceptable. The ARIMA models forecast 1319 mm of mean annual rainfall, 1203 mm of mean annual ETo, and 0.82 of mean annual DI from 2015 to 2024. The results obtained from this research can guide development of water-management practices and cropping systems in the area that rely on this weather station. The approaches used and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed and a model fitted at other locations.

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Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

Construction Economics and Building ◽

10.5130/ajceb.v18i1.5604 ◽

2018 ◽

Vol 18 (1) ◽

pp. 70-82

Author(s):

Ernest Kissi ◽

Theophilus Adjei-Kumi ◽

Peter Amoah ◽

Jerry Gyimah

Keyword(s):

Price Index ◽

Predictive Accuracy ◽

Early Stage ◽

Moving Average ◽

Predictive Ability ◽

Integrated Modelling ◽

Final Decision ◽

Exogenous Variables ◽

Vector Error Correction ◽

Autoregressive Integrated Moving Average

Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI) is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

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Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

10.1101/2020.12.18.20248340 ◽

2020 ◽

Author(s):

Messis Abdelaziz ◽

Adjebli Ahmed ◽

Ayeche Riad ◽

Ghidouche Abderrezak ◽

Ait-Ali Djida

Keyword(s):

Moving Average ◽

Arima Model ◽

Arima Models ◽

Forecast Period ◽

Autoregressive Integrated Moving Average ◽

Hospital Reform ◽

Estimated Parameters ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method

ABSTRACTCoronavirus disease has become a worldwide threat affecting almost every country in the world. The aim of this study is to identify the COVID-19 cases (positive, recovery and death) in Algeria using the Double Exponential Smoothing Method and an Autoregressive Integrated Moving Average (ARIMA) model for forecasting the COVID-19 cases.The data for this study were obtained from March 21st, 2020 to November 26th, 2020. The daily Algerian COVID-19 confirmed cases were sourced from The Ministry of Health, Population and Hospital Reform of Algeria. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the COVID-19 case in Algeria following the ARIMA model (0,1,1). Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria.

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ANALISA PERAMALAN WINTER’S EXPONENTIAL SMOOTHING BROWN DIBANDINGKAN DENGAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE PADA DATA JUMLAH KEJADIAN HIPERTENSI

Jurnal Ilmu Kesehatan ◽

10.24929/jik.v4i1.695 ◽

2019 ◽

Vol 4 (1) ◽

pp. 1-10

Author(s):

Wisoedhanie Widi .A ◽

Nanik Dwi A.

Keyword(s):

Community Health Centers ◽

Moving Average ◽

Secondary Data ◽

Exponential Smoothing ◽

Smoothing Method ◽

Preventive Action ◽

Autoregressive Integrated Moving Average ◽

Planning And Control ◽

Exponential Smoothing Method ◽

And Control

In an attempt to see and examine the situation and conditions that occur in the future to do forecasting (forecasting). Hypertension is a major disease in the ten Clinics Together and almost every month new hypertension cases occur, so the incidence of hypertension is becoming the trend and forecasting needs to be done. The purpose of this research is to do forecasting on the data the number of incident hypertension in Clinics With the city of Malang with Exponential Smoothing method using winter's Brown compared to Autoregressive Integrated Moving Arima. This type of research is the study of non reactive (non reactive research) which is a type of secondary data for research.Unit samples in this research are patients who come for the medication and patients in Clinics With hypertension Malang. in 2013 to 2016. Research data using Minitab software. The results of this study showed that both methods of forecasting results shows that tend to decrease in the year 2018 with the lowest incidence in December that as many as 58 incidents on Exponential Smoothing method of winter's and some 80 events on the method of Autoregressive Integrated Moving Average. The existence of a trend of decrease in the incidence of hypertension can be supported by the growing health services at community health centers With has been doing various efforts in preventive action, promotif and collaborative in the handling of problems Hypertension.Through these research results, it is advisable to draw up a health center party planning and control and eradication programs work for transmission of diseases of hypertension (P2P) with reference to the results of the forecasting incidence of hypertension in the year 2018.

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Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)

Journal of Data Analysis ◽

10.24815/jda.v1i1.11874 ◽

2018 ◽

Vol 1 (1) ◽

pp. 21-31

Author(s):

Nany Salwa ◽

Nidya Tatsara ◽

Ridha Amalia ◽

Aja Fatimah Zohra

Keyword(s):

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Secondary Data ◽

Series Data ◽

Forecasting Model ◽

Virtual Currency ◽

Autoregressive Integrated Moving Average ◽

The Mean ◽

Level 2

ABSTRAK. Bitcoin merupakan mata uang virtual yang saat ini banyak diminati sebagai alternatif investasi. Metode ARIMA adalah salah satu metode yang digunakan untuk peramalan data deret waktu. Tujuan dari penelitian ini adalah untuk membuat model dan meramalkan harga bitcoin. Data yang digunakan adalah data sekunder yaitu berupa data harga bitcoin selama 60 periode mulai dari tanggal 10 Januari 2018 sampai dengan 10 Maret 2018 untuk memprediksikan harga bitcoinselama 30 periode kedepan mulai tanggal 11 Maret 2018 sampai dengan 09 April 2018. Dari hasil penelitian menunjukkan bahwa data harga bitcoin selama 60 periode tidak memenuhi asumsi stasioneritas terhadap rata-rata untuk itu dilakukan proses differencing tingkat 2 agar data menjadi stasioner. Model ARIMA yang dihasilkan adalah ARIMA(0,2,1) yaitu Zt = μ - 0,9647Zt-1 + at dan model tersebut cocok digunakan untuk peramalan data harga bitcoin. Hasil peramalan dengan menggunakan model ARIMA(0,2,1) menunjukkan bahwa harga bitcoin untuk 30 periode kedepannya mengalami penurunan secara perlahan dan hasil peramalan mendekati data sebenarnya. ABSTRACT. Bitcoin is a virtual currency that is currently much interested as an alternative investment. ARIMA method is one of the methods used for forecasting time series data. The purpose of this research is to create a model and predicted the price of the bitcoin. The data used are secondary data that is in the form of price bitcoin during 60 periods starting from January 10, 2018 up to 10 March 2018 to predict price bitcoin for 30 the next periods began March 11 and ended on 9 April 2018 2018. Based on the results of the study showed that the price of bitcoin during 60 periods did not fullfiled the assumptions of stasioneritas towards the mean. Therefore using the differencing level 2 process, so the data becomes stationary. The result of ARIMA model is ARIMA(0, 2, 1) Zt = μ - 0,9647Zt-1 + at and the model fits the data used for forecasting price bitcoin. The results of the forecasting model using ARIMA (0, 2, 1) shows that the price of the bitcoin for 30 periods has decreased gradually and forecasting results close to the actual data.

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Peramalan Jumlah Penumpang Pesawat Di Bandara Sultan Iskandar Muda Dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average)

Journal of Data Analysis ◽

10.24815/jda.v1i1.11847 ◽

2018 ◽

Vol 1 (1) ◽

pp. 1-11

Author(s):

Fara Inka Durrah ◽

Yulia Yulia ◽

Tessa Prihartina Parhusip ◽

Asep Rusyana

Keyword(s):

Moving Average ◽

Secondary Data ◽

Final Conclusion ◽

Seasonal Patterns ◽

Autoregressive Integrated Moving Average ◽

International Airport

ABSTRAK. Penelitian ini bertujuan untuk mengetahui model peramalan dimana jumlah keberangkatan penumpang pada PT. Angkasa Pura II (Perseron). Kantor cabang bandar udara Internasional Iskandar Muda dengan menggunakan Metode Seasonal Autoregresive Integrated Moving Average (SARIMA). Data jumlah keberangkatan di bandar udara Internasional Iskandar muda merupakan data dengan pola musiman. Data yang digunakan merupakan data sekunder yang diperoleh dari Bandara Sultan Iskandar Muda periode Bulan Januari 2010 hingga Desember 2016. Model terbaik yang diperoleh yaitu ARIMA (0,1,1)(0,0,1)12. Sedangkan berdasarkan data peramalan yang diperoleh dapat diketahui bahwa diprediksi jumlah penumpang pesawat tetinggi pada tahun 2017 akan terjadi pada Bulan Desember, dan jumlah penumpang pesawat terendah diprediksikan akan terdapat pada Bulan Maret 2017. Kesimpulan akhir yang diperoleh yaitu jumlah penumpang pada Tahun 2017 akan mengalami peningkatan dibandingkan dengan Tahun sebelumnya.ABSTRACT. This study aims to determine the model of forecasting where the number of passengers at PT. Angkasa Pura II (Perseron). International branch of Iskandar Muda International Airport using Seasonal Autoregresive Integrated Moving Average (SARIMA) method. Data on the number of departures at Iskandar International Airport are young data with seasonal patterns. The data used are secondary data obtained from Sultan Iskandar Muda Airport during January 2010 to December 2016. The best model is ARIMA (0,1,1) (0,0,1)12. While based on forecasting data obtained can be seen that predicted the number of passengers in 2017 will occur in December, and the lowest number of passengers is predicted to be in March 2017. Final conclusion obtained that the number of passengers in the Year 2017 will increase compared with the previous Year.

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