scholarly journals Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models

2020 ◽  
Vol 13 (2) ◽  
pp. 287-294 ◽  
Author(s):  
Huan Liu ◽  
Chenxi Li ◽  
Yingqi Shao ◽  
Xin Zhang ◽  
Zhao Zhai ◽  
...  
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Autoregressive Integrated Moving Average ◽  
Acute Hemorrhagic Conjunctivitis

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

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

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

Optimization of pumping schedule based on water demand forecasting using a combined model of autoregressive integrated moving average and exponential smoothing

2014 ◽  
Vol 15 (1) ◽  
pp. 188-195 ◽  
Author(s):  
Hyeong-Seok Kang ◽  
Hyunook Kim ◽  
Jaekyeong Lee ◽  
Ingyu Lee ◽  
Byoung-Youn Kwak ◽  
...  
Keyword(s):  
Water Supply ◽  
Moving Average ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Water Supply Systems ◽  
Combined Model ◽  
Autoregressive Integrated Moving Average ◽  
Electricity Cost ◽  
Water Demands ◽  
Water Pumping

Stable water supply to end users is the most important element in water supply systems (WSSs). The portion of energy used by the water distribution system is up to 40% of the total energy consumed by WSSs. To save energy cost for pumping systems, a number of attempts have been made. Especially, an optimization scheme for scheduling the water-pumping operation has attracted the interest of water engineers. In this paper, a binary integer program was applied to optimize pumping schedule of a WSS in Polonnaruwa, Sri Lanka based on the hourly water demands for the next day. The water demands were forecasted by a combined model consisting of an autoregressive integrated moving average (ARIMA) model and an error compensation routine based on exponential smoothing technique. The result showed that the optimization system could reduce the operation cost of the WSS by minimizing electricity for water pumping; electricity cost for pump operation could be reduced by 55%.

scholarly journals Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis

2020 ◽  
Vol 148 ◽  
Author(s):  
Hongfang Qiu ◽  
Dewei Zeng ◽  
Jing Yi ◽  
Hua Zhu ◽  
Ling Hu ◽  
...  
Keyword(s):  
Moving Average ◽  
Development Trend ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Peak Period ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Incidence Data ◽  
The Mean ◽  
Actual Incidence

Abstract Acute haemorrhagic conjunctivitis is a highly contagious eye disease, the prediction of acute haemorrhagic conjunctivitis is very important to prevent and grasp its development trend. We use the exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model to analyse and predict. The monthly incidence data from 2004 to 2017 were used to fit two models, the actual incidence of acute haemorrhagic conjunctivitis in 2018 was used to validate the model. Finally, the prediction effect of exponential smoothing is best, the mean square error and the mean absolute percentage error were 0.0152 and 0.1871, respectively. In addition, the incidence of acute haemorrhagic conjunctivitis in Chongqing had a seasonal trend characteristic, with the peak period from June to September each year.

scholarly journals PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA

2020 ◽  
Vol 1 (1) ◽  
pp. 37-46
Author(s):  
YULINAR I. AJUNU ◽  
NOVIANITA ACHMAD ◽  
MUHAMMAD REZKY FRIESTA PAYU
Keyword(s):  
Optimal Parameter ◽  
Moving Average ◽  
Model Identification ◽  
Accurate Method ◽  
Exponential Smoothing ◽  
Autoregressive Integrated Moving Average ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Value Determination ◽  
Data Plotting

As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value.  The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value.

scholarly journals An Assessment of Renewable Energy in Bangladesh through ARIMA, Holt’s, ARCH-GARCH Models

2012 ◽  
Vol 60 (2) ◽  
pp. 159-162
Author(s):  
Fatema Tuz Jhohura ◽  
Md. Israt Rayhan
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Industrial Sector ◽  
Exponential Smoothing ◽  
Information Criteria ◽  
Optimal Decision ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt’s linear exponential smoothing, and the autoregressive conditional heteroscedastic (with their extensions to generalized autoregressive conditional heteroscedastic) models were fitted to the data. The goodness of fit is performed via the Akaike information criteria, Schwartz Bayesian criteria. It was established that the generalized autoregressive conditional heteroscedastic model was superior to the autoregressive integrated moving average model and Holt’s linear exponential smoothing because the data was characterized by changing mean and variance.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11486 Dhaka Univ. J. Sci. 60(2): 159-162, 2012 (July)

scholarly journals PERBANDINGAN EXPONENTIAL SMOOTHING HOLT-WINTERS DAN ARIMA PADA PERAMALAN PRODUKSI PADI DI PROVINSI GORONTALO

2019 ◽  
Vol 7 (1) ◽  
pp. 20-26
Author(s):  
Ingka Rizkyani Akolo
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Autoregressive Integrated Moving Average

Gorontalo merupakan salah satu provinsi di Indonesia yang memiliki lahan pertanian yang besar yang sebagian besar ditanami padi. Kebutuhan bahan pangan padi di Gorontalo bertambah dari tahun ke tahun sesuai dengan pertambahan penduduk. Akan tetapi, karena terjadi perbedaan hasil panen padi di setiap daerah mengakibatkan kelangkaan beras sehingga mempengaruhi pemenuhan kebutuhan dan stabilitas penyediaan pangan di Gorontalo. Untuk membuat perencanaan terkait komoditas akan pangan diperlukan model matematika khusus untuk peramalan. Salah satunya model yang sering digunakan untuk peramalan adalah dalam peramalan adalah metode metode exponential smoothing Holt-Winters dan Autoregressive Integrated Moving Average (ARIMA). Tujuan dari penelitian ini adalah untuk mengetahui model peramalan produksi padi di Provinsi Gorontalo menggunakan metode exponential smoothing Holt-Winters dan ARIMA sehingga dapat memberikan masukan kepada Pemerintah Daerah dalam mengambil kebijakan yang berkaitan dengan ketahanan pangan di Provinsi Gorontalo. Hasil analisis menunjukkan bahwa model peramalan terbaik adalah Model peramalan produksi padi dengan metode exponential smoothing Holt-Winters (αlpha = 0.5, gamma = 0.3, delta = 0,1)  yang memberikan nilai RMSE lebih kecil dibandingkan metode ARIMA.

scholarly journals Deseasonalisation in Electricity Load Forecasting

2018 ◽  
Vol 7 (4.30) ◽  
pp. 448
Author(s):  
Maria Elena Binti Nor ◽  
Mohd Saifullah Rusiman ◽  
Suliadi Firdaus Sufahani ◽  
Mohd Asrul Affendi Abdullah ◽  
Sathwinee A/P Bataraja ◽  
...  
Keyword(s):  
Moving Average ◽  
Load Forecasting ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Forecast Performance ◽  
Autoregressive Integrated Moving Average ◽  
Electricity Load ◽  
Seasonal Data ◽  
Electricity Load Forecasting

Nowadays, there is an increasing demand for electricity however overproduction of electricity lead to wastage. Therefore, electricity load forecasting plays a crucial role in operation, planning and maintenance of power system. This study was designed to investigate the effect of deseasonalisation on electricity load data forecasting. The daily seasonality in electricity load data was removed and the forecast methods were employed on both the seasonal data and non-seasonal data. Holt Winters method and Seasonal-Autoregressive Integrated Moving Average (SARIMA) methods were used on the seasonal data. Meanwhile, Simple and Double Exponential Smoothing methods as well as Autoregressive Integrated Moving Average (ARIMA) methods were used on the non-seasonal data. The error measurement that were used to assess the forecast performance were mean absolute error (MAE) and mean absolute percentage error (MAPE). The results revealed that both Exponential Smoothing method and Box-Jenkins method produced better forecast for deseasonalised data. Besides, the study proved that Box-Jenkins method was better in forecasting electricity load data for both seasonal and non-seasonal data.

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