Journal articles on the topic 'ARIMA/SARIMA'
To see the other types of publications on this topic, follow the link: ARIMA/SARIMA.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'ARIMA/SARIMA.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Putri, Syifania, and A'yunin Sofro. "Peramalan Jumlah Keberangkatan Penumpang Pelayaran Dalam Negeri di Pelabuhan Tanjung Perak Menggunakan Metode ARIMA dan SARIMA." MATHunesa: Jurnal Ilmiah Matematika 10, no. 1 (April 30, 2022): 61–67. http://dx.doi.org/10.26740/mathunesa.v10n1.p61-67.
Full textAbstract:
Seiring berkembangnya zaman, Indonesia memiliki perkembangan yang pesat dalam bidang tranportasi, khusunya transportasi laut yaitu kapal terbang. Arus transportasi laut yang ramai juga dirasakan oleh Pelabuhan Tanjung Perak yang berada di Surabaya. Adanya fluktuasi dan terjadi penurunan jumlah penumpang di Pelabuhan Tanjung Perak pada bulan Februari 2020 akibat munculnya virus COVID-19 yang memberi akibat pada bidang pariwisata dan bidang transportasi untuk menutup sementara beroperasinya sektor tersebut. Adapaun penelitian ini bertujuan untuk memprediksi jumlah penumpang pelayaran dengan metode Autoregressive Integrated Moving Average (ARIMA) dan metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Metode ARIMA dan SARIMA memiliki data berfluktuasi yang berbeda pola. Metode ARIMA dengan pola yang menunjukkan fluktuasi yang tidak tetap dan metode SARIMA dengan pola musiman. Dengan prediksi ini diharapkan dapat membantu sektor yang ada di Pelabuhan untuk mengantisipasi kenaikan dan penuruna penumpang, mempersiapkan sarana prasarana, dan menyediakan fasilitas yang memadai. Penelitian ini menganalisis jumlah keberangkatan penumpang pelayaran di Pelabuhan Tanjung Perak sebagai data sekunder dari situs resmi Badan Pusat Statistik dengan periode Januari 2014 sampai dengan November 2021. Model yang terpilih pada metode ARIMA yaitu ARIMA(1,1,1) sedangkan pada metode SARIMA yaitu SARIMA(1,1,1)(2,0,0)12. Hasil penelitian menunjukkan analisis dengan metode ARIMA mempunyai nilai akurasi lebih kecil daripada analisis dengan menggunakkan metode SARIMA yaitu sebesar 16.15% dan merupakan metode terbaik untuk peramalan ini. Kata Kunci: Peramalan, ARIMA, SARIMA
2
Vishwakarma, Sagar, and Dr S. C. Solanki. "Predicting sales during COVID using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2481–89. http://dx.doi.org/10.22214/ijraset.2022.41822.
Full textAbstract:
Abstract: The purpose of this study is to compare VAR, ARIMA and SARIMA methods in an attempt to generate sales forecasting in Store xyz with high accuracy. This study will compare the results of sales forecasting with time series forecasting model of Vector Auto Regression (VAR), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). VAR or ARIMA model still accurate when the time series data is only in a short period, these models is accurate on short period forecasting but less accurate on long period forecasting. Meanwhile Seasonal Autoregressive Integrate Moving Average is more accurate on forecasting seasonal time series data, either it’s pattern shows trend or not all three models are compared with forecasting data showing seasonal patterns. The data used is the data of super mart retail store, sales from 2017 to 2022. Accuracy level of each model is measured by comparing the percentage of forecasting value with the actual value. This value is called Mean Absolute Deviation (MAD). Based on the comparison result, the best model with the smallest MAD value is SARIMA model (0,1,0) (0,1,0)12 with MAD value 0.122. From the comparison results can be concluded that the SARIMA model is optimal to be used as a model for further forecasting Keywords: Machine Learning, sales prediction, ARIMA, SARIMA, VAR, PYTHON, Anaconda navigator, Jupiter notebook.
3
Prendin, Francesco, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, and Jorge Bondia. "Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions." Sensors 22, no. 22 (November 10, 2022): 8682. http://dx.doi.org/10.3390/s22228682.
Full textAbstract:
Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.
4
Ningsih, Prawati, Maiyastri Maiyastri, and Yudiantri Asdi. "PERAMALAN JUMLAH KEDATANGAN WISATAWAN MANCANEGARA KE SUMATERA BARAT MELALUI BANDARA INTERNASIONAL MINANGKABAU DENGAN MODEL SARIMA." Jurnal Matematika UNAND 8, no. 2 (July 15, 2019): 128. http://dx.doi.org/10.25077/jmu.8.2.128-134.2019.
Full textAbstract:
Jumlah kedatangan wisatawan mancanegara ke Sumatera Barat melalui Bandara Internasional Minangkabau cenderung mengalami perubahan di setiap tahunnya. Untuk mengetahui jumlah kedatangan wisatawan mancanegara di masa yang akan datang, dapat dilakukan dengan menggunakan model SARIMA. Model SARIMA merupakan model ARIMA yang mengandung unsur musiman. Model ini diaplikasikan untuk meramalkan jumlah kedatangan wisatawan mancanegara pada periode Januari 2019 hingga Desember 2019. Hasil analisis data menunjukkan bahwa model SARIMA(1, 0, 1)(2, 1, 0)12 yang terbaik, dimana hasil pendugaan yang diperoleh tidak jauh berbeda dari data aktual.Kata Kunci: Wisatawan Mancanegara, Model SARIMA, Peramalan
5
Othman, Mahmod, Rachmah Indawati, Ahmad Abubakar Suleiman, Mochammad Bagus Qomaruddin, and Rajalingam Sokkalingam. "Model Forecasting Development for Dengue Fever Incidence in Surabaya City Using Time Series Analysis." Processes 10, no. 11 (November 19, 2022): 2454. http://dx.doi.org/10.3390/pr10112454.
Full textAbstract:
Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.
6
ARUAN, SARA SEPTIANA. "The PERBANDINGAN METODE ARIMA DAN SARIMA DALAM PERAMALAN PENJUALAN KELAPA." JAMI: Jurnal Ahli Muda Indonesia 2, no. 2 (December 20, 2021): 79–90. http://dx.doi.org/10.46510/jami.v2i2.82.
Full textAbstract:
Objektif. Peramalan dilakukan untuk memprediksi kejadian yang akan terjadi dimasa depan berdasarkan data masa lalu. Dalam hal ini dilakukan peramalan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan untuk membantu UKM tersebut memprediksi penjualan kelapa di tengah pendemi Covid 19. Peramalan dilakukan untuk memprediksi penjualan kelapa pada bulan Januari -Maret 2021. Selanjutnya, hasil peramalan yang diperoleh akan divalidasi dengan data aktual penjualan kelapa. Validasi ini bertujuan untuk mengetahui apakah metode peramalan yang digunakan sesuai untuk meramalkan penjualan di UKM Pak Balen Pasar Kandak Medan. Jika hasil peramalan dan validasi sesuai maka metode peramalan yang dipilih tepat untuk digunakan sebagai metode peramalan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan.
Material and Metode. Metode peramalan yang digunakan untuk meramalkan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan adalah metode ARIMA dan SARIMA. Kemudian dilakukan serangkaian uji untuk memilih metode yang tepat yang terdiri dari uji stasioneritas ragam, uji stasioneritas rata-rata, uji white noise, pemodelan sementara, dan uji signifikansi.
Hasil. Berdasarkan pengolahan data penjualan kelapa di UKM Pak Balen Pasar Kandak Medan diperoleh peramalan dengan metode ARIMA yang sesuai untuk meramalkan penjualan kelapa pada bulan Januari – Maret 2021 masing-masing adalah 520 kelapa, 486 kelapa, dan 459 kelapa. Sedangkan, metode SARIMA berdasarkan pengolahan data tidak memenuhi untuk peramalan, karena pada pengolah data uji signifikansi tidak terdapat model SARIMA yang signifikan.
Kesimpulan. Metode yang paling sesuai untuk meramalkan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan adalah metode ARIMA dengan model (1, 1, 1). Dari validasi peramalan ARIMA dan penjualan aktual maka dapat disimpulkan bahwa metode ARIMA sesuai untuk meramalkan penjualan kelapa di UKM Pak Balen Pasar Kandak Medan. Karena hasil peramalan ARIMA mendekati data penjualan kelapa aktual pada bulan Januari – Maret 2021 yaitu masing-masing 572 kelapa, 490 kelapa, dan 451 kelapa.
7
Ruhiat, Dadang, and Adang Effendi. "PENGARUH FAKTOR MUSIMAN PADA PEMODELAN DERET WAKTU UNTUK PERAMALAN DEBIT SUNGAI DENGAN METODE SARIMA." TEOREMA 2, no. 2 (March 31, 2018): 117. http://dx.doi.org/10.25157/.v2i2.1075.
Full textAbstract:
Pemodelan dan peramalan deret waktu saat ini berkembang dan banyak digunakan di berbagaibidang termasuk di bidang hidrologi. Salah satu parameter hidrologi yang sangat penting adalahdebit sungai. Besaran dan fluktuasi debit sungai pada periode waktu tertentu sangat dipengauhioleh faktor musiman, yaitu musim hujan dan kemarau. Penelitian ini dilakukan untuk mengetahuipengaruh faktor musiman terhadap kemampuan model dalam menirukan dan meramalkan perilakudari data debit sungai. Pemodelan dilakukan berbasis kepada pendekatan metode statistik BoxJenkins, yaitu Autoregressive Integrated Moving Average (ARIMA) dengan melibatkan faktormusiman dalam pemodelannya, yang dikenal dengan model Seasonal Autoregressive IntegratedMoving Average (SARIMA). Model yang digunakan untuk peramalan adalah model yang terbaik,yaitu model yang memenuhi syarat signifikansi parameter, white noise dan memiliki nilai MAPE(Mean Absolute Percentage Error)yang terkecil. Perbandingan dilakukan terhadap hasilperamalan model-model terbaik, masing-masing terbaik dari model SARIMA dan model ARIMAnon musiman, sehingga dapat diketahui pengaruh faktor musiman terhadap hasil pemodelan danperamalan. Hasil analisis menunjukan ternyatamodel SARIMA terbaik lebih layak digunakandaripada model Arima non musiman.
8
Perone, Gaetano. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries." Econometrics 10, no. 2 (April 9, 2022): 18. http://dx.doi.org/10.3390/econometrics10020018.
Full textAbstract:
The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 46 out 48 metrics (in forecasting future values), i.e., on 95.8% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.
9
YAHYA, ARYA. "PERAMALAN INDEKS HARGA KONSUMEN INDONESIA MENGGUNAKAN METODE SEASONAL-ARIMA (SARIMA)." Jurnal Gaussian 11, no. 2 (August 28, 2022): 313–22. http://dx.doi.org/10.14710/j.gauss.v11i2.35528.
Full textAbstract:
The pattern of changes in the Consumer Price Index (CPI) is very important to observe from time to time because it is closely related to economic indicators such as the amount of money in circulation, exchange rates, interest rates, and other economic indicators. This study aims to form a model and predict the Indonesian Consumer Price Index using the SARIMA method. The data used in modeling are monthly CPI data for the period January 2012 to February 2022. The best model for predicting Indonesia's CPI is the SARIMA (0,1,1)(0,1,1)12 model. This study examines the CPI value in January and February 2022 which is not included in the estimation model, the estimation results (108,08 and 108,20) are very close to the actual CPI value issued by the Central Statistics Agency.
10
Silalahi, Desri Kristina. "Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province." JTAM | Jurnal Teori dan Aplikasi Matematika 4, no. 1 (April 24, 2020): 76. http://dx.doi.org/10.31764/jtam.v4i1.1888.
Full textAbstract:
The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.
11
Khoiri, Halwa Annisa, Aan Zainal Muttaqin, and Dika Restu Elyuda. "Analisis Peramalan Permintaan Darah di Unit Transfusi Darah Kota Madiun." Prosiding Seminar Nasional Teknik Industri 1 (October 4, 2021): 24–32. http://dx.doi.org/10.33479/snti.v1i.119.
Full textAbstract:
Unit Donor Darah (UDD) Kota Madiun melayani transfusi darah sekaligus menyediakan darah dari permintaan yang berasal dari Bank Darah Rumah Sakit (BDRS) maupun langsung dari Rumah Sakit di wilayah Kota Madiun dan sekitarnya. Pada tahun 2020, UDD telah berhasil memenuhi 97% permintaan darah, namun karena penyebaran virus Covid-19 yang semakin meluas maka diperlukan peramalan permintaan darah sampai akhir tahun 2021. Berdasarkan analisis data permintaan pada tahun sebelumnya diperoleh model peramalan terbaik untuk permintaan darah A adalah SARIMA(2,1,0)(0,1,1)12 dengan nilai MAPE 9,35%, model peramalan untuk permintaan darah B adalah SARIMA(1,1,0)(0,1,1)12 dengan MAPE 13,25%, model peramalan untuk permintaan darah AB adalah ARIMA(2,1,1) dengan MAPE 22,46%, dan model peramalan untuk permintaan darah O adalah ARIMA(0,1,1).
12
Iswari, Anistia, Yenni Angraini, and Mohammad Masjkur. "Comparison of The SARIMA Model and Intervention in Forecasting The Number of Domestic Passengers at Soekarno-Hatta International Airport." Indonesian Journal of Statistics and Its Applications 6, no. 1 (May 31, 2022): 132–46. http://dx.doi.org/10.29244/ijsa.v6i1p132-146.
Full textAbstract:
The Covid-19 pandemic has had a massive effect on the air transportation sector. Soekarno-Hatta International Airport (Soetta) skilled a lower variety of passengers because of the Covid-19 pandemic, even though Soetta Airport persisted to perform normally. Forecasting the number of passengers needs to be done by the airport to decide the proper policy. Therefore, the airport wishes to estimate the range of passengers to determine the right coverage and prepare the facilities provided if there may be a boom withinside the range of passengers throughout the Covid-19 pandemic. Forecasting the number of domestic passengers at Soetta Airport on this examination makes use of the SARIMA model and intervention. This examination compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The effects confirmed that the best SARIMA model became ARIMA ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55,18% and 588887.4, respectively. The best intervention model became ARIMA0,1,1) (1,0,0)12 b = 0, s = 5, r = 1 with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values acquired suggest that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport throughout the Covid-19 pandemic.
13
Wang, H., C. W. Tian, W. M. Wang, and X. M. Luo. "Time-series analysis of tuberculosis from 2005 to 2017 in China." Epidemiology and Infection 146, no. 8 (April 30, 2018): 935–39. http://dx.doi.org/10.1017/s0950268818001115.
Full textAbstract:
AbstractSeasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA–GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA–GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA–GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.
14
Lee, Jen-Yu, Tien-Thinh Nguyen, Hong-Giang Nguyen, and Jen-Yao Lee. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe." Energies 15, no. 11 (May 29, 2022): 4003. http://dx.doi.org/10.3390/en15114003.
Full textAbstract:
Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with MAPEEurope−ARIMA = 0.05, and MAPEUSA−ARIMA=0.05. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.
15
Ghide, Luwam, Siyuan Wei, and Yiming Ding. "Comparative Study of Wavelet-SARIMA and EMD-SARIMA for Forecasting Daily Temperature Series." International Journal of Analysis and Applications 20 (March 18, 2022): 17. http://dx.doi.org/10.28924/2291-8639-20-2022-17.
Full textAbstract:
This paper aims to find a forecasting model based on the comparative study of wavelet- ARIMA and EMD-ARIMA models and residual-based model selection technique for temperature time series. Time series analysis is essential in studying temperature data for investigating the variation and predicting the future trend, in which we can control the changes and make good decisions. And most important is to understand the trend in the series with time. This study applied hybridized models of wavelet transform and empirical mode decomposition with seasonal autoregressive integrated moving average (SARIMA), which combines two models to get better accuracy, for forecasting daily average temperature time series data in the central region of Eritrea, Asmara. Daily data was collected for 30 years, from January 1, 1991, to December 31, 2020. The study compares WT-SARIMA and EMD-SARIMA models to find a well fit and better forecasting model. Model selection techniques are essential for time series analysis to determine which model best fits our data. AIC and BIC are the most used methods in model selection. This paper uses an additional method based on the residual series. In estimating accurate parameters, the structure of the residual sequence had a lot of connection, in which a stationary residual depict an accurate estimation. From this perspective, a nonstationarity measurement of the residual series is used for model selection. The relative performance is based on the predictive capability of sample forecasts assessed. The results indicate that the hybridized wavelet-SARIMA model is more effective than the other models, and MATLAB soft-wire is used for this analysis.
16
Amelia, Ririn, Elyas Kustiawan, Ineu Sulistiana, and Desy Yuliana Dalimunthe. "FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (March 21, 2022): 137–46. http://dx.doi.org/10.30598/barekengvol16iss1pp137-146.
Full textAbstract:
Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variables and seasonal elements, namely the SARIMAX model (Seasonal ARIMA with eXogenous input). Based on the analysis that has been carried out, the prediction of rainfall in Pangkalpinang City, Bangka Belitung Islands Province can be modeled with the SARIMAX model (0,1,3)(0,1,1){12} for monthly rainfall and SARIMAX (0,1,2 )(0,1,3){12} for maximum daily rainfall. When compared with the actual data and previous studies using ARIMAX, the SARIMAX model is still better in the forecasting process when compared to the ARIMAX model. If viewed based on the AIC value of the SARIMA model, the SARIMAX model is also more suitable to be used to predict rainfall in Pangkalpinang City.
17
López, Danilo A., Carlos Andrés Martínez Alayón, Edward Johannes Uribe Sierra, and Nicolás Carlos Eduardo Torres Vallejo. "Modelado de pérdidas en una transmisión de video por medio de series de tiempo ARIMA y SARIMA." Revista Tecnura 17, no. 37 (September 18, 2013): 53. http://dx.doi.org/10.14483/udistrital.jour.tecnura.2013.3.a05.
Full textAbstract:
Este artículo presenta los resultados obtenidos al representar las pérdidas en una transmisión de video digital por medio de modelos ARIMA y SARIMA, siguiendo la metodología Box-Jenkins y haciendo uso del lenguaje de programación R para la estimación de los coeficientes.Se hizo una comparación de estos dos modeloscon el fin de determinar cuál es el más apropiado para representar la serie original y estimar valores futuros, encontrando que el modelo SARIMA presenta un mejor ajuste y predice de mejor manera el comportamiento de la misma.
18
Prianda, Bayu Galih, and Edy Widodo. "PERBANDINGAN METODE SEASONAL ARIMA DAN EXTREME LEARNING MACHINE PADA PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 4 (December 1, 2021): 639–50. http://dx.doi.org/10.30598/barekengvol15iss4pp639-650.
Full textAbstract:
Bali Island of the Gods is one of the wealth of very popular tourist destinations and has the highest number of foreign tourists in Indonesia. It is very necessary to do more in-depth learning related to the projections or forecasting of foreign tourist visits to Bali at a certain period of time. Forecasting analysis used is to compare two methods, namely the Seasonal ARIMA method (SARIMA) and Extreme Learning Machine (ELM). The SARIMA method is a statistical method commonly used in forecasting time series data that contains seasonality and has good accuracy. While the ELM method is a new learning method of artificial neural networks that has fast learning speed and good accuracy. The results obtained indicate that the Seasonal ARIMA method is a better method used to predict the number of tourists to Bali in this case, because it has a smaller forecasting MAPE value of 4.97%. While the ELM method has a forecasting MAPE value of 7.62%.
19
FM, Mohammed Farooq Abdulla, Tamilselvan V, Harshini V S, and Deepthikka R S. "Purchase and Analytics for Grace Marketing." International Journal of Engineering Research in Computer Science and Engineering 9, no. 5 (May 14, 2022): 21–24. http://dx.doi.org/10.36647/ijercse/09.05.art003.
Full textAbstract:
In recent years development of computer systems were able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data is known as machine learning.In this phase sales of different lubricants were predicted using a multivariate time series forecasting algorithm.Previously it showed that the model was accurate in predicting the engine oil sales for a particular time.Using Regressions the accuracy of sales prediction was less (74%) and the models like SVM and Random forest were showing signs of over fitting.The accuracy obtained in the multivariate time series forecasting was good than other algorithms.Time series algorithms are used extensively for forecasting time-based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast time based data.SARIMAX are efficient in forecasting data which has seasonality trends than ARIMA which are good in forecasting data which is stationary in nature Time series methods are extensively used for forecasting time based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast tie based data.ARIMA is the abbreviation of Auto Regressive Integrated Moving Average a model which explains a given time series model based on its lags and other values.SARIMAX is the abbreviation of Seasonal Auto Regressive Integrated Moving Average with Xegeneous variables. ARIMA model is best for forecasting stationary time series data and SARIMAX is used for forecasting values which is seasonal in nature.
20
FM, Mohammed Farooq Abdulla, Tamilselvan V, Harshini V S, and Deepthikka R S. "Purchase and Analytics for Grace Marketing." International Journal of Science, Engineering and Management 9, no. 4 (April 25, 2022): 1–4. http://dx.doi.org/10.36647/ijsem/09.04.a001.
Full textAbstract:
In recent years development of computer systems were able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data is known as machine learning.In this phase sales of different lubricants were predicted using a multivariate time series forecasting algorithm.Previously it showed that the model was accurate in predicting the engine oil sales for a particular time.Using Regressions the accuracy of sales prediction was less (74%) and the models like SVM and Random forest were showing signs of over fitting.The accuracy obtained in the multivariate time series forecasting was good than other algorithms.Time series algorithms are used extensively for forecasting time-based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast time based data.SARIMAX are efficient in forecasting data which has seasonality trends than ARIMA which are good in forecasting data which is stationary in nature Time series methods are extensively used for forecasting time based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast tie based data.ARIMA is the abbreviation of Auto Regressive Integrated Moving Average a model which explains a given time series model based on its lags and other values.SARIMAX is the abbreviation of Seasonal Auto Regressive Integrated Moving Average with Xegeneous variables. ARIMA model is best for forecasting stationary time series data and SARIMAX is used for forecasting values which is seasonal in nature.
21
Adineh, Amir Hossein, Zahra Narimani, and Suresh Chandra Satapathy. "Importance of data preprocessing in time series prediction using SARIMA: A case study." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 4 (January 18, 2021): 331–42. http://dx.doi.org/10.3233/kes-200065.
Full textAbstract:
Over last decades, time series data analysis has been in practice of specific importance. Different domains such as financial data analysis, analyzing biological data and speech recognition inherently deal with time dependent signals. Monitoring the past behavior of signals is a key for precise predicting the behavior of a system in near future. In scenarios such as financial data prediction, the predominant signal has a periodic behavior (starting from beginning of the month, week, etc.) and a general trend and seasonal behavior can also be assumed. Autoregressive Integrated Moving Average (ARIMA) model and its seasonal extension, SARIMA, have been widely used in forecasting time-series data, and are also capable of dealing with the seasonal behavior/trend in the data. Although the behavior of data may be autoregressive and trends and seasonality can be detected and handled by SARIMA, the data is not always exactly compatible with SARIMA (or more generally ARIMA) assumptions. In addition, the existence of missing data is not pre-assumed in SARIMA, while in real-world, there can be always missing data for different reasons such as holidays for which no data may be recorded. For different week days, different working hours may be a cause of observing irregular patterns compared to what is expected by SARIMA assumptions. In this paper, we investigate the effectiveness of applying SARIMA on such real-world data, and demonstrate preprocessing methods that can be applied in order to make the data more suitable to be modeled by SARIMA model. The data in the existing research is derived from transactions of a mutual fund investment company, which contains missing values (single point and intervals) and also irregularities as a result of the number of working hours per week days being different from each other which makes the data inconsistent leading to poor result without preprocessing. In addition, the number of data points was not adequate at the time of analysis in order to fit a SARIM model. Preprocessing steps such as filling missing values and tricks to make data consistent has been proposed to deal with existing problems. Results show that prediction performance of SARIMA on this set of real-world data is significantly improved by applying several preprocessing steps introduced in order to deal with mentioned circumstances. The proposed preprocessing steps can be used in other real-world time-series data analysis.
22
M. K., SHARMA, OMER MOHAMMED, and KIANI SARA. "Time series analysis on precipitation with missing data using stochastic SARIMA." MAUSAM 71, no. 4 (August 4, 2021): 617–24. http://dx.doi.org/10.54302/mausam.v71i4.45.
Full textAbstract:
This paper presents an application of the Box-Jenkins methodology for modeling the precipitation in Iran. Linear stochastic model known as multiplicative seasonal ARIMA was used to model the monthly precipitation data for 44 years. Missing data occurred in between for 34 months for some reason. To fill the gap a SARIMA model was fitted based on the first 180 available observations and the missing observations were substituted by the forecasts for the next 34 months. Then a SARIMA model was fitted for the full data. The result showed that the fitted model represent the full data well.
23
Sunariadi, Noviati Maharani, Putroue Keumala Intan, Dian Candra Rini Novitasari, and Yuni Hariningsih. "PREDIKSI PRODUKSI BAWANG MERAH DI KABUPATEN NGANJUK DENGAN METODE SEASONAL ARIMA (SARIMA)." Transformasi : Jurnal Pendidikan Matematika dan Matematika 6, no. 1 (June 22, 2022): 49–60. http://dx.doi.org/10.36526/tr.v6i1.1672.
Full textAbstract:
Produksi bawang merah merupakan komoditas holtikultura yang dikembangkan secara nasional dengan pembinaan yang intensif. Faktor utama yang mempengaruhi produksi bawang merah adalah varietas benih, lahan dan cuaca. Penelitian ini bertujuan untuk memprediksi produksi bawang merah agar komoditas bawang merah dapat menjaga kestabilan harga dan ketersediaan barang di kabupaten Nganjuk. Data pada penelitian ini bersumber dari BPS kabupaten Nganjuk yang digunakan dalam membangun model terbaik dengan metode SARIMA untuk memprediksi produksi bawang merah periode 2021-2023. Berdasarkan hasil analisis yang dilakukan, model terbaik adalah model SARIMA (3,0,2)(2,1,2)12 yang memiliki nilai MAPE sebesar 2,01%.
24
Pang, Yi-Hui, Hong-Bo Wang, Jian-Jian Zhao, and De-Yong Shang. "Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling." Geofluids 2020 (October 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/8851475.
Full textAbstract:
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.
25
Xu, Feng, Yu-Ang Du, Hong Chen, and Jia-Ming Zhu. "Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model." Complexity 2021 (March 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/5553935.
Full textAbstract:
Herring and mackerel are two of the most important pillars of Scottish fisheries. In recent years, global warming has caused a gradual rise in ocean temperatures. In order to survive and reproduce, herring and mackerel populations will migrate. This will have a huge impact on Scotland’s fisheries. Therefore, we need to predict the relocation of fish stocks in advance, make timely adjustments to the fishing range, and minimize the loss of the fishing industry. In this article, we subdivide the research target sea area into 39 regions, establish the optimal SARIMA model for each region based on the collected seawater temperature time series data, and take region 13 and region 15 as examples to fit the ARIMA (3, 3, 1) (1, 2, 1) and ARIMA (2, 3, 1) (0, 2, 1) models with a period of 12. The results show that the SARIMA model fits well in all regions and predicts the temperature changes in the studied sea area from 2021 to 2050. Finally, according to the predicted sea temperature in different periods, the migration position of the fish school is predicted.
26
M., Manikandan, Vishnu Prasad R., Amit Kumar Mishra, Rajesh Kumar Konduru, and Newtonraj A. "Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model." International Journal Of Community Medicine And Public Health 5, no. 9 (August 24, 2018): 3962. http://dx.doi.org/10.18203/2394-6040.ijcmph20183579.
Full textAbstract:
Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.
27
Kim, Kyeong-Rae, Jae-Eun Park, and Il-Tae Jang. "Outpatient forecasting model in spine hospital using ARIMA and SARIMA methods." Journal of Hospital Management and Health Policy 4 (September 2020): 20. http://dx.doi.org/10.21037/jhmhp-20-29.
Full text
28
Wisodewo, M. D., H. A. Rosyid, and A. R. Taufani. "Forecasting chicken meat and egg in indonesia using ARIMA and SARIMA." Jurnal Informatika 16, no. 1 (January 15, 2022): 8. http://dx.doi.org/10.26555/jifo.v16i1.a25416.
Full text
29
Diarsih, Inas Husna, Tarno Tarno, and Agus Rusgiyono. "PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM." Jurnal Gaussian 7, no. 3 (August 29, 2018): 281–92. http://dx.doi.org/10.14710/j.gauss.v7i3.26661.
Full textAbstract:
Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS
30
Mulya, Dila, Yudiantri Asdi, and Ferra Yanuar. "PENERAPAN METODE HOLT WINTER DAN SEASONAL ARIMA PADA PERAMALAN PERKEMBANGAN WISATAWAN MANCANEGARA YANG DATANG KE INDONESIA." Jurnal Matematika UNAND 6, no. 4 (December 1, 2017): 29. http://dx.doi.org/10.25077/jmu.6.4.29-36.2017.
Full textAbstract:
Abstrak. Pada tugas akhir ini akan dirumuskan pemodelan peramalan perkembanganwisatawan mancanegara yang datang ke Indonesia dengan metode Holt Winter dan Sea-sonal ARIMA. Kemudian hasil peramalan perkembangan wisatawan dengan menggu-nakan kedua metode tersebut akan dibandingkan berdasarkan nilai Mean Squared Devi-ation (MSD), Mean Absolute Percentage Error (MAPE) serta Mean Absolute Deviation(MAD). Berdasarkan hasil yang diperoleh, model terbaik untuk peramalan perkem-bangan wisatawan mancanegara yang datang ke Indonesia adalah model SARIMA(0; 1; 1)(1; 1; 0)12 , karena nilai MAPE, MAD dan MSD yang diperoleh lebih kecil dari-pada model Holt Winter.Kata Kunci: Holt Winter, Seasonal Arima, Trend, Musiman
31
Hendayanti, Ni Putu Nanik, and Maulida Nurhidayati. "Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali." Jurnal Varian 3, no. 2 (April 30, 2020): 149–62. http://dx.doi.org/10.30812/varian.v3i2.668.
Full textAbstract:
Berbagai sumber pendapatan yang dapat dihasilkan dalam suatu daerah, salah satunya yaitu dalam sektor pariwisata. Seperti halnya sektor yang lain, sektor pariwisata juga memberikan banyak sumbangan bagi pembangunan ekonomi di suatu daerah maupun negara tujuan wisata. Indonesia memiliki banyak tujuan wisata daerah yang sudah terkenal hingga mancanegara salah satunya yaitu Pulau Bali. Bali merupakan daerah yang sudah memiliki kedudukan yang sejajar dengan daerah-daerah tujuan wisata lainnya yang ada di dunia. Sebagai suatu daerah yang sangat berpotensi dalam pengembangan wisata, maka pemerintah memberikan perhatian yang khusus dalam pengembangan pariwisata di Pulau Bali. Maka dari itu, perlu adanya peramalan jumlah kunjungan wisatawan mancanegara ke Bali yang nantinya bisa bermanfaat bagi pemerintah daerah maupun dinas pariwisata. Dalam hal ini, akan digunakan dua metode untuk meramalkan jumlah kunjungan wisatawan mancanegara ke Bali. Adapun metode yang digunakan yaitu Seasonal ARIMA dan Support Vector Regression (SVR). Hasil peramalan data out sampel dengan menggunakan metode SARIMA dan SVR menunjukkan bahwa metode SARIMA memiliki nilai MAPE lebih kecil dari pada SVR. Nilai MAPE motode SARIMA adalah 5,33% sedangkan metode SVR sebesar 19,74%. Begitu juga nilai MSE dan MAE dari metode SARIMA lebih kecil dari metode SVR. Dari Penelitian yang dilakukan dapat disimpulkan bahwa model SARIMA merupakan motode yang lebih baik untuk memprediksi jumlah kunjungan wisatawan mancanegara ke Bali.
32
Purwandari, Agustina Elisa Dyah. "Pemodelan Dan Peramalan Indeks Harga Konsumen (IHK) Kota Sampit Dengan Seasonal Arima (Sarima)." Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika 6, no. 2 (January 11, 2020): 61–72. http://dx.doi.org/10.31316/j.derivat.v6i2.497.
Full textAbstract:
AbstractSampit is one of 82 cities in Indonesia which calculate inflation. Inflation is an increase of prices on goods and services in a region. Government’s control is very important because inflation relates to the real income, the exchange rate, import exports, and so on. Inflation is based on the Consumer Price Index (CPI). Because of CPI is a monthly data prices, it is highly influenced by seasonal factors. Therefore, CPI data modelling is needed because it helps the government to make appropriate policies. Method that can be used for time series data with seasonal influences is Seasonal Autoregressive Integrated Moving Average (SARIMA). The results of the study show that the right model for Sampit’s CPI is SARIMA with the order p = 1, d = 1, P = 1, D = 1, Q = 1, s = 12. It is the best model that can built and be used for forecasting because with 95 percent of confidence, the model explains 87.23 percent of data. Forecasting in this research use interval analysis and found that January 2020 may be the highest increase of CPI (inflation) in 2020. Keywords: CPI, Inflation, SARIMA
33
Atmanegara, Eviyana. "Peramalan Ekspor Karet Provinsi Jambi dengan Model Seasonal ARIMA." Jurnal Ilmiah Ilmu Terapan Universitas Jambi|JIITUJ| 6, no. 2 (December 26, 2022): 263–77. http://dx.doi.org/10.22437/jiituj.v6i2.22964.
Full textAbstract:
The industrial sector contributed the most to total non-oil and gas exports in Jambi Province. Rubber and its processed products are the main export commodities in the industrial sector. The export value of rubber and its products increased in 2017 but decreased again from 2018 to 2020. The decline in exports harmed the economy in Jambi Province. It is necessary to predict the export value of rubber and its products in the coming period. Forecasting is done on time series data using the ARIMA and SARIMA models. The best model for modeling the export value of rubber and its products in Jambi Province is SARIMA (0,1,1)(0,0,1)12. It is predicted that the export value of rubber and its processed products will decrease by around 4 percent in 2022. And decrease by around 5 percent in 2023. The results of this forecast can be used as an early warning in making policies related to exports in the coming period.
34
Assakhiy, Rasyada, Samsul Anwar, and A. R. Fitriana. "PERAMALAN REALISASI PENERIMAAN ZAKAT PADA BAITULMAL ACEH DENGAN MEMPERTIMBANGKAN EFEK DARI VARIASI KALENDER." Jurnal Ekonomi Pembangunan 27, no. 2 (December 31, 2019): 27–45. http://dx.doi.org/10.14203/jep.27.2.2019.27-45.
Full textAbstract:
Baitulmal Aceh merupakan sebuah lembaga pemerintah daerah Provinsi Aceh yang bertanggung jawab sebagai pengelola dan pendistribusi zakat, infak dan sedekah (ZIS). Peramalan potensi zakat pada masa yang akan datang dibutuhkan oleh Baitulmal Aceh sebagai salah satu landasan penyusunan kebijakan pengelolaan ZIS. Penelitian ini bertujuan untuk meramalkan potensi zakat yang terkumpul pada tahun 2018 dan 2019 dengan mempertimbangkan efek dari variasi kalender. Data yang digunakan dalam penelitian ini adalah data realisasi penerimaan zakat bulanan mulai dari bulan Januari 2015 hingga Desember 2017 yang diperoleh dari Baitulmal Aceh. Data tersebut dianalisis dengan model Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) dan Seasonal Autoregressive Integrated Moving Average (SARIMA) sebagai model pembanding. Hasil penelitian menunjukkan bahwa model ARIMAX dengan orde ARIMA(2,0,2) (1,0,2)12, t, V1, ..., V11 jauh lebih baik daripada model SARIMA dengan orde ARIMA(0,1,2)(0,1,1)12 berdasarkan indikator ketepatan hasil ramalannya (RMSE dan MAPE). Realisasi penerimaan zakat pada tahun 2018 dan 2019 masing-masing diperkirakan sebesar Rp. 1.347.526.504 dan Rp. 1.359.728.268. Hasil peramalan tersebut dapat digunakan sebagai salah satu rujukan bagi Baitulmal Aceh dalam menyusun kebijakan pendistribusian zakat pada tahun-tahun yang akan datang.
35
Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Si Zhao. "Predictive Efficiency Comparison of ARIMA-Time-Series and the Grey System GM(1,1) Forecast Model on Forecasting the Incidence Rate of Hepatitis B." Advanced Materials Research 709 (June 2013): 836–39. http://dx.doi.org/10.4028/www.scientific.net/amr.709.836.
Full textAbstract:
To compare the stochastic autoregressive integrated moving average (ARIMA) model and the grey system GM(1,1) model to predict the hepatitis B incidence in Qianan. Considering the Box-Jenkins modeling and GM(1,1) model approach, hepatitis B incidence was collected monthly from 2004 to 2011, a SARIMA model and a gray system GM(1,1) model were fit. Then, these models were used for calculating hepatitis B incidence for the last 6 observations compared with observed data. The constructed models were performed to predict the monthly incidence rate in 2013. The model SARIMA(0,1,1)(0,1,1)12 and was established finally and the residual sequence was a white noise sequence. Using Excel 2003 to establish the gray system GM(1,1) model of hepatitis B incidence and evaluating the accuracy of the mode as well as forecasting. By posterior-error-test (C=0.435, p=0.821) and residual test, the model accuracy was qualified. It was necessary and practical to apply the approach of ARIMA model in fitting time series to predict hepatitis within a short lead time. The prediction results showed that the hepatitis B incidence in 2013 had a slight upward trend.
36
Nurfadilah, Nanda. "The ANALISIS PERAMALAN PERMINTAAN PRODUK MINUMAN HERBAL DENGAN METODE ARIMA PADA CV. GENTONG MAS." JAMI: Jurnal Ahli Muda Indonesia 2, no. 2 (December 22, 2021): 117–23. http://dx.doi.org/10.46510/jami.v2i2.85.
Full textAbstract:
CV. Gentong Mas terletak di Kampung Sadang Lebak, Situsari, Karangpawitan, Garut. CV. Gentong Mas merupakan salah satu perusahaan yang memproduksi minuman herbal, produknya dinamakan gentong mas dan guchie mas. Akan tetapi pada penelitian ini, yang akan diteliti hanya produk gentong mas saja. Permasalahan yang dihadapi perusahaan yaitu permintaan yang tidak stabil dan tidak adanya peramalan untuk periode kedepannya Hal ini dikarenakan CV Gentong Mas putus kontrak kerja sama dengan distributor tunggal PT. Oriya Khazanah Sejahtera. Penyebab putus kontrak tersebut dikarenakan terjadi permasalahan di PT Oriya Khazanah Sejahtera sendiri. Pada penelitian ini metode yang digunakan yaitu Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Holt-Winters sebagai pembanding. tiga Metode tersebut digunakan untuk mengetahui hasil peramalan mana yang terbaik untuk 1 tahun mendatang, terhitung sejak September 2020 hingga Agustus 2021. Hasil peramalan ini di lihat dari 2 aspek, yaitu Nilai MSE (Mean Squared Error) terkecil dan uji validasi. Berdasarkan hasil pengolahan data diketahui Nilai MSE terkecil yaitu metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan nilai MSE sebesar 4,705,580. Akan tetapi ketika di uji validasi, hasil peramalan yang paling mendekati permintaan sebenarnya selama 4 periode (bulan) yaitu metode Autoregressive Integrated Moving Average (ARIMA). Berdasarkan hasil pengolahan data peramalan menggunakan aplikasi minitab, perbandingan nilai MSE dan uji validasi hasil peramalan menunjukkan bahwa metode terbaik ialah metode arima. Karena hasil peramalannya mendekati hasil permintaan yang sebenarnya.
37
Versoza, Enrique Raphael, Sofia Elaine Romarate, and Aboy,Jacque Bon-Isaac. "Forecasting Korean Arrivals in the Philippines." Volume 4 - 2019, Issue 9 - September 4, no. 9 (September 12, 2020): 37–45. http://dx.doi.org/10.38124/ijisrt20sep081.
Full textAbstract:
This paper investigates the rise of South Korean tourism in the Philippines from 2014 to 2018 and explain its behavior year-to-year, and the other part is to forecast it’s growth or decline in the next following years; all of this is done through a Seasonal ARIMA (SARIMA) modelling framework. Results reveal that Korean arrivals were best modelled through a ARIMA(1,0,0)(2,1,0)₁₂ model, with residuals that are randomly distributed and contain no autocorrelations and an AICc value of -36.18, the lowest among the tested variations of the model, the model is the most appropriate to forecast the data for a 3-year period.
38
Onyeka-Ubaka, J. N., M. A. Halid, and R. K. Ogundeji. "Optimal Stochastic Forecast Models of Rainfall in South-West Region of Nigeria." International Journal of Mathematical Analysis and Optimization: Theory and Applications 7, no. 2 (November 16, 2021): 1–20. http://dx.doi.org/10.52968/28306097.
Full textAbstract:
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.
39
Salauddin Khan, Md, Masudul Islam, Sajal Adhikary, Md Murad Hossain, and Sohani Afroja. "Analysis and Predictions of Seasonal Affected Weather Variables of Bangladesh: SARIMA Models vs. Traditional Models." International Journal of Business and Management 13, no. 12 (November 12, 2018): 70. http://dx.doi.org/10.5539/ijbm.v13n12p70.
Full textAbstract:
Bangladesh is a semi-tropical country, categorized by widespread seasonal disparities in rainfall, temperature, and humidity. Seasonality has been an input aspect of time series modeling when taking into account weather variables. In terms of multiple features of the weather variables i.e. randomness, cyclical variation and trend, time series methods etc. ARIMA can be a superior preference but, weather variables are affected by seasonality. Thinking about the grimy meadow, this paper presents Seasonal Auto-regressive Moving Average (SARIMA) model that takes seasonal and cyclical variation over the years. This study also aims to compare traditional methods like Single Exponential Method, Double Exponential Method, and Holt Winter Method with the SARIMA model. Time series plots, month plots, and B-B plots are used for identifying seasonal effect clearly. For seasonal stationary checking, Canova Hansen Stationary test has been utilized. Then, the order of the variables is identified, ACF and PACF have been checked and estimated preeminent order for these variables by AIC and Log-likelihood. Finally, Single Exponential Method, Double Exponential Method, and Holt Winter Method are introduced for comparing and forecasting. The proposed models SARIMA(0,0,0)(1,0,3)12, SARIMA(0,0,0)(1,0,1)12, SARIMA(0,0,0)(1,0,2)12 and SARIMA(0,0,0)(1,0,1)12 for maximum and minimum temperature, rainfall and humidity on the basis of Akaike Information Criteria and Log likelihood have been captured most seasonality of the data. Comparing them with traditional methods, traditional methods give a better result than the acquired model based on error measurement. So, traditional methods give a better estimate than the SARIMA models for selected weather variables, with lower mean square error, RMSE, MAE and MASE.
40
Colther, Cristian, and Ailin Arriagada-Millaman. "Pronóstico de la demanda turística de Chile basado en modelos lineales y no lineales estacionales." PASOS. Revista de Turismo y Patrimonio Cultural 19, no. 2 (2021): 323–36. http://dx.doi.org/10.25145/j.pasos.2021.19.021.
Full textAbstract:
TIn this paper, the Chilean tourism demand experienced during the period 2000‑2018 has been modelled using a linear regression model with dichotomous variables (MRL) and an ARIMA model with a seasonal component (SARIMA). The results show that the SARIMA approach is more effective in replicating the non‑stationary, non‑linear behaviour and the presence of seasonality in the series, with the forecasts obtained from this model presenting a low rate of error, in this case ‑5.6% for outbound tourism and ‑5.9% for inbound tourism. In consequence, this approach may be an effective tool for forecasting forecast tourism demand in the short term, and support for planning and management in the sector in the face of fluctuations in tourism demand.
41
BOZKURT, Kurtuluş, Aytaç PEKMEZCİ, and Hatice ARMUTCUOGLU TEKİN. "Forecasting Tourism Demand By Box-Jenkins Method: The Case of Türkiye." Anatolia: Turizm Araştırmaları Dergisi 33, no. 2 - Ön Yayımdaki Makaleler (January 1, 2022): 77–86. http://dx.doi.org/10.17123/atad.1087573.
Full textAbstract:
Türkiye’nin en önemli gelir kaynaklarından biri olan turizm sektöründe gelecek dönemlerdeki olası turizm talebinin belirlenmesi; kaynakların dağıtılması ve planlanması, doğru fiyat politikalarının tespit edilmesi ve uygun pazarlama tekniklerinin seçilmesi açısından önem arz etmektedir. Bu nedenle, bu çalışmada Ekim 2021 ve Eylül 2022 (2021:10 – 2022:09) tarihlerindeki Türkiye’ye yönelik turizm talebinin tahmin edilmesi amaçlanmıştır. Bu bağlamda turizm talebinin tahmin edilmesinde vekil değişken olarak 1990:01 - 2021:09 zaman aralığında Türkiye’ye gelen turist sayıları verileri kullanılmıştır. Belirtilen dönemde gerçekleşmesi beklenen turizm talebinin tahmin edilmesinde Box-Jenkins yöntemi altında ele alınan ARIMA (entegre otoregresif hareketli ortalama modeli) ve SARIMA (mevsimsel entegre otoregresif hareketli ortalama modeli) modelleri uygulanmıştır. Çalışmanın sonucunda en iyi uyum gösteren modelin, SARIMA (2,1,2)(2,1,0) modeli olduğu belirlenmiştir. Çalışmada ayrıca gelecek 12 aya yönelik öngörü yapılmıştır.
42
Koziński, Witold, and Tomasz Świst. "Short-Term Currency in Circulation Forecasting for Monetary Policy Purposes – The Case of Poland." e-Finanse 11, no. 1 (March 1, 2015): 65–75. http://dx.doi.org/10.1515/fiqf-2016-0107.
Full textAbstract:
Abstract One of the most significant factors which influences the level of banking sector liquidity is Currency in Circulation. Although the central bank is in charge of distribution of the currency it can’t assess the demand for the currency, as that demand is generated by the customers of commercial banks. Therefore, the amount of Currency in Circulation has to be modelled and forecasted. This paper introduces ARIMA(2,1) and SARIMA(2,1)(5,0) models with dummy variables and discusses its applicability to the forecasting of Currency in Circulation. The forecasting performance of these models is compared. The results indicate that the performance of SARIMA(2,1)(5,0) is better and that both models might be applied for monetary policy purposes as supportive tools for banking sector liquidity forecasting.
43
Martinez, Edson Zangiacomi, and Elisângela Aparecida Soares da Silva. "Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model." Cadernos de Saúde Pública 27, no. 9 (September 2011): 1809–18. http://dx.doi.org/10.1590/s0102-311x2011000900014.
Full textAbstract:
This study aimed to develop a forecasting model for the incidence of dengue in Ribeirão Preto, São Paulo State, Brazil, using time series analysis. The model was performed using the Seasonal Autoregressive Integrated Moving Average (SARIMA). Firstly, we fitted a model considering monthly notifications of cases of dengue recorded from 2000 to 2008 in Ribeirão Preto. We then extracted predicted values for 2009 from the adjusted model and compared them with the number of cases observed for that year. The SARIMA (2,1,3)(1,1,1)12 model offered best fit for the dengue incidence data. The results showed that the seasonal ARIMA model predicts the number of dengue cases very effectively and reliably, and is a useful tool for disease control and prevention.
44
Dharmadhikari, Pratiksha Rajendra. "SARIMA – A Model for Forecasting Product order demand." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1284–89. http://dx.doi.org/10.22214/ijraset.2021.38575.
Full textAbstract:
Abstract: Product analysis is the most important part for any working manufacturing. It provides the sales record of their currently manufactured product and also it helps to predict its performance in the future. For this analysis, a SARIMAX model has been used with Time series forecasting. This paper will explain the need of such model instead of using a simple regression model to predict the order demand. This study analyses and presents a forecasting model to predict an order demand for the Product over the time period. Demand in Product is a main component for planning all processes in supply chain, and therefore determining Product demand is a great interest for supply chain. Mean forecasting for product order demand was carried out using SARIMA model, by using the past data from the period of 2011 to 2017. The model with the least value of Akaike Information Criterion (AIC) was selected as the appropriate model for forecasting mean Error. Test for normality of residuals were performed to see the adequacy of the chosen model. SARIMA (1, 1, 1) (0, 1, 1) (12) was selected as the best model for mean product order demand forecast. The results obtained will prove that the model could be utilized to forecast the future demand in the Product manufacturing industry. These results will help the manufacturers for manufacturing reliable guidelines in making decisions. Keywords: ARIMA, AIC, S-ARIMA, Regression
45
Ranganai, Edmore, and Mphiliseni B. Nzuza. "A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data." Journal of Energy in Southern Africa 26, no. 1 (March 23, 2015): 125–37. http://dx.doi.org/10.17159/2413-3051/2015/v26i1a2215.
Full textAbstract:
Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.
46
SAHA, ENAKSHI, ARNAB HAZRA, and PABITRA BANIK. "SARIMA modeling of the monthly average maximum and minimum temperatures in the eastern plateau region of India." MAUSAM 67, no. 4 (December 8, 2021): 841–48. http://dx.doi.org/10.54302/mausam.v67i4.1411.
Full textAbstract:
The SARIMA time series model is fitted to the monthly average maximum and minimum temperature data sets collected at Giridih, India for the years 1990-2011. From the time-series plots, we observe that the patterns of both the series are quite different; maximum temperature series contain sharp peaks in almost all the years while it is not true for the minimum temperature series and hence both the series are modeled separately (also for the sake of simplicity). SARIMA models are selected based on observing autocorrelation function (ACF) and partial autocorrelation function (PACF) of the monthly temperature series. The model parameters are obtained by using maximum likelihood method with the help of three tests [i.e., standard error, ACF and PACF of residuals and Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and corrected Akaike Information Criteria (AICc)]. Adequacy of the selected models is determined using diagnostic checking with the standardized residuals, ACF of residuals, normal Q-Q plot of the standardized residuals and p-values of the Ljung-Box statistic. The models ARIMA (1; 0; 2) × (0; 1; 1)12 and ARIMA (0; 1; 1) × (1; 1; 1)12 are finally selected for forecasting of monthly average maximum and minimum temperature values respectively for the eastern plateau region of India.
47
Chechi, Leonardo, and Fábio M. Bayer. "Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS." Revista Brasileira de Engenharia Agrícola e Ambiental 16, no. 12 (December 2012): 1321–29. http://dx.doi.org/10.1590/s1415-43662012001200009.
Full textAbstract:
Este trabalho apresenta uma análise de séries temporais dos dados de temperatura mínima e temperatura máxima mensal da cidade de Erechim, RS; apresenta-se uma comparação de duas classes de modelos tradicionais de previsão, nomeadamente: modelos da classe ARIMA e modelos de alisamento exponencial. Na classe de modelos ARIMA foram selecionados, utilizando-se critérios de informação, modelos do tipo SARIMA, que consideram a característica sazonal da temperatura do ar; já para os modelos de alisamento exponencial utilizaram-se os modelos Holt-Winters aditivo, em que as constantes de alisamento são determinadas de forma a minimizar o erro quadrático médio entre valores previstos e observados; esta análise permitiu a identificação de componentes como sazonalidade e períodos atípicos. Os modelos de previsão foram comparados para diferentes horizontes de previsão, sendo que os modelos da classe ARIMA se mostraram mais acurados. Os modelos ajustados se mostraram adequados para traçar previsões das variáveis de temperatura do ar, mostrando-se importantes ferramentas para a climatologia agrícola.
48
Villanueva, Bayron, Danilo López-Sarmiento, and Edwin Rivas-Trujillo. "Revisión De Los Principales Métodos De Modelamiento Y Predicción De Tráfico Orientados A Plataformas De Transmisión De Video E IPTV Usando Series De Tiempo." Revista científica 2, no. 16 (June 26, 2013): 10. http://dx.doi.org/10.14483/23448350.4019.
Full textAbstract:
En este artículo se hace una investigación de las principales técnicas que existen para modelar y predecir el tráfico de video de forma estadística, enfocándose en los modelos que usan series de tiempo con el fin de identificar cuáles de estos se acomodan mejor al tráfico estocástico representativo de los sistemas IPTV. Para tal fin, se hace una introducción al análisis a través de series de tiempo, y una presentación del estado del arte acerca de modelamiento de tráfico de video sobre redes de datos. De la investigación se concluye que, de los modelos que permiten describir y predecir el tráfico futuro sobre redes de datos, los que se ajustan en una mayor medida a sistemas IPTV son modelos basados en series ARIMA, de estos, el modelo SARIMA podría describir de forma más precisa las tendencias periódicas del tráfico IPTV.AbstractThis paper, intends to review the most important techniques that allow performing statistic video traffic modeling and forecasting, focusing in time series models, so we can identify which models are better to describe the representative IPTV stochastic traffic. For this purpose, we make a short introduction to time series analysis, and a review of the state of the art on video traffic modeling over data networks. From this research we conclude that, of all the available models to describe and forecast network traffic, the more appropriate to use within IPTV systems are ARIMA time series models, from which SARIMA model are the best option.ResumoEste artigo tem como objetivo revisar as principais técnicas existentes para a modelagem e previsão de tráfego estatisticamente vídeo, com foco em modelos usando séries temporais, a fim de identificar quais destes são o tráfego estocástico mais adequado representante sistemas IPTV. Para este fim, uma breve introdução à análise por meio de séries temporais, e uma revisão do estado da arte em modelagem de tráfego de vídeo através de redes de dados. A investigação concluiu que, dos modelos para descrever e prever o futuro de tráfego em redes de dados, que são ajustados a uma maior extensão de sistemas de IPTV são baseados em modelos da série ARIMA, estes modelo SARIMA poderia descrever em mais preciso do tráfego periódico tendências IPTV.
49
C., Asogwa Oluchukwu, Eze C.M., and Okonkwo C. R. "On the Modelling of Road Traffic Crashes: A case of SARIMA Models." Journal of Advance Research in Mathematics And Statistics (ISSN: 2208-2409) 5, no. 8 (August 31, 2018): 15–35. http://dx.doi.org/10.53555/nnms.v5i8.532.
Full textAbstract:
This paper examined the modeling of accident cases in four major roads leading to the main city of Enugu State of Nigeria using SARIMA Models. Among the most robust approaches for analysing time series data is the Autoregressive Integrated Moving Average (ARIMA) model propounded by Box and Jenkins (1979). In this paper, we employed the Box-Jenkins methodology to build SARIMA model for the accident cases for the period, January 2007 to December 2015 with a total of 108 data points. The model obtained in this paper was used to forecast monthly cases of accident in each of the roads for the upcoming year 2016. The forecasted results will help Government and Federal road safety commission to see how to maintain orderliness on the roads to reduce the case of road traffic crashes along the roads
50
Valipour, Mohammad. "Long-term runoff study using SARIMA and ARIMA models in the United States." Meteorological Applications 22, no. 3 (February 9, 2015): 592–98. http://dx.doi.org/10.1002/met.1491.
Full text