scholarly journals The ANALISIS PERAMALAN PERMINTAAN PRODUK MINUMAN HERBAL DENGAN METODE ARIMA PADA CV. GENTONG MAS

2021 ◽  
Vol 2 (2) ◽  
pp. 117-123
Author(s):  
Nanda Nurfadilah
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Squared Error

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.

MATRIX FORMULAS FOR NONSTATIONARY ARIMA SIGNAL EXTRACTION

2008 ◽  
Vol 24 (4) ◽  
pp. 988-1009 ◽  
Author(s):  
Tucker McElroy
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Moving Average ◽  
Signal Extraction ◽  
Error Signal ◽  
Autoregressive Integrated Moving Average ◽  
General Matrix ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Moving Average Processes

The paper provides general matrix formulas for minimum mean squared error signal extraction for a finitely sampled time series whose signal and noise components are nonstationary autoregressive integrated moving average processes. These formulas are quite practical; in addition to being simple to implement on a computer, they make it possible to easily derive important general properties of the signal extraction filters. We also extend these formulas to estimates of future values of the unobserved signal, and we show how this result combines signal extraction and forecasting.

scholarly journals COVID-19 Infection Forecasting based on Deep Learning in Iran

2020 ◽  
Author(s):  
Mehdi Azarafza ◽  
Mohammad Azarafza ◽  
Jafar Tanha
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
National Level ◽  
Absolute Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Error Metrics ◽  
Squared Error ◽  
Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

scholarly journals Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
pp. 101-110
Author(s):  
Evizal Abdul Kadir ◽  
Nur Ezzati Dayana ◽  
Sri Listia Rosa ◽  
Mahmod Othman ◽  
Rizauddin Saian
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Mean Squared Error ◽  
Moving Average ◽  
Arima Model ◽  
Forest Damage ◽  
Autoregressive Integrated Moving Average ◽  
Squared Error ◽  
Proposed Model ◽  
Forecasting System

Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.

scholarly journals A Nonlinear Optimizated PSO-SVR Hybrid System for Time Series Forecasting with ARIMA

2021 ◽  
Author(s):  
Emilly Pereira Alves ◽  
Joao Fausto Lorenzato Oliveira ◽  
Manoel Henrique da Nóbrega Marinho ◽  
Francisco Madeiro
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Moving Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Linear Pattern ◽  
Squared Error ◽  
The Subject

In the forecasting time series field, the combination of techniques to aid in predicting different patterns has been the subject of several studies. Hybrid models have been widely applied in this scenario, where the vast majority of series are composed of linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) presents satisfactory results in a linear pattern prediction but can not capture nonlinear ones. In dealing with nonlinear patterns, the Support Vector Regression (SVR) has shown promising results. In order to map both patterns, an optimized nonlinear combination model based on SVR and ARIMA is proposed. The main difference in comparison with other works is the use of an interactive Particle Swarm Optimization (PSO) to increase the prediction performance. To the experimental setup, six well-known datasets of the literature is used. The performance is assessed by the metrics Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results show the proposed system attains better outcomes when compared to the other tested techniques, for most of the used data.

scholarly journals Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 41
Author(s):  
Jaka Darma Jaya
Keyword(s):  
Trend Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Polynomial Trend ◽  
Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri.  Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional.  Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis.  Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error).  Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12;  MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

PERENCANAAN PERSEDIAAN BAHAN BAKU AKAR TANJUNG DENGAN METODE MATERIAL REQUIREMENTS PLANNING (MRP) PADA UPT. MAKARTI POMOSDA

2020 ◽  
Vol 16 (3) ◽  
pp. 1-12
Author(s):  
Khoirul Hidayah ◽  
Sukarni Sukarni ◽  
Achmad Syaichu
Keyword(s):  
Standard Error ◽  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Material Requirements Planning ◽  
Safety Stock ◽  
Mean Error ◽  
Squared Error ◽  
Material Requirement

Suatu produksi yang direncanakan dengan baik akan menghasilkan efektivitas dan efisiensi produksi bagi perusahaan. Pentingnya perencanaan material pada perusahaan diharapkan dapat menghasilkan sistem yang baik terhadap proses produksi. Tujuan dari penelitian ini adalah untuk mengetahui penerapan Material Requirement Planning (MRP) sehingga kebutuhan bahan baku selama proses produksi di UPT MAKARTI POMOSDA dapat terpenuhi dengan menggunakan metode peramalan forecasting dalam satu tahun yaitu, moving average dan weighted moving average.  Metode ini terpilih untuk mengetahui safety stock nya produk setiap bulan dan setiap tahun. Berdasarkan detail dan analisa kesalahan metode moving average dengan menggunakan program POM QM forWindows Versi 3 Basic (Mean Error) 42,455, MAD (Mean Absolute Deviation) 259,545, MSE (Mean Squared Error) 118490,6, Standard Error (denom=n-2=9) 380,555, MAPE (Mean Absolute Percent Error) 643, dan next period 480. Sedangkan detail dan analisa kesalahan metode ini dengan menggunakan program POM QM For Windows Versi 3 Basic (Mean Error) 38,827, MAD (Mean Absolute Deviation) 212,257, MSE (Mean Squared Error) 83586,58, Standard Error (denom=n-2=9) 323,239, MAPE (Mean Absolute Percent ) 495, dan next period 464,893. Berdasarkan hasil proses diatas juga diketahui (safety stock) pada UPT MAKARTI POMOSDA pada tahun 2017 yaitu sejumlah 5209 unit, setelah dilakukan penelitian mengalami kenaikan sebesar 6758 dengan prosentase sebesar 129,7%, sehingga tidak ada penumpukan barang digudang. Hal ini juga didukung dengan penurunan biaya simpan bahan baku dari Rp 120.850/Periode (bulan) menjadi Rp 109.350/Periode (bulan).

Anticipation of Demand in Supply Chains

2019 ◽  
pp. 1-45
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Moving Average ◽  
Demand Management ◽  
Inventory Level ◽  
Squared Error ◽  
Interfirm Collaboration ◽  
Overall Performance

The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.

scholarly journals ACCRUED FORECASTING ON TOURIST’S ARRIVAL IN BANGLADESH FOR SUSTAINABLE DEVELOPMENT

2021 ◽  
Vol 36 (2spl) ◽  
pp. 708-714
Author(s):  
Sayed Mohibul HOSSEN ◽  
◽  
Mohd Tahir ISMAIL ◽  
Mosab I. TABASH ◽  
Ahmed ABOUSAMAK ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Mean Squared Error ◽  
Moving Average ◽  
Critical Role ◽  
Absolute Error ◽  
Tourism Industry ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Autoregressive Integrated Moving Average ◽  
Private And Public

Forecasting of potential tourists’ appearance could assume a critical role in the tourism industry, arranging at all levels in both the private and public sectors. In this study our aim to build an econometric model to forecast worldwide visitor streams to Bangladesh. For this purpose, the present investigation focuses on univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling. Model choice criteria were Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (RMSE). As per descriptive statistics, the mean appearances were 207012 and will be 656522 (application) every year. Mean Absolute Deviation and Mean Squared Deviation likewise concurred with MAPE, MAE, and MSE. The result reveals that for sustainable development the SARIMA model is the reasonable model for forecasting universal visitor appearances in Bangladesh.

scholarly journals PREDIKSI JUMLAH PENERIMAAN SISWA SMK SWASTA TAHUN AJARAN 2011/2012

2012 ◽  
Vol 3 (2) ◽  
pp. 923
Author(s):  
Haryadi Sarjono
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Vocational High School ◽  
Absolute Deviation ◽  
Squared Error ◽  
Forecasting Methods ◽  
Naive Method ◽  
Manual Calculation ◽  
Weighted Moving Average

This study aims to determine prediction number of modern private Vocational High School (SMK) students in a province in Borneo with the approach of six forecasting methods: Linear Regression, Exponential Smoothing with Trend, Exponential Smoothing, Weighted Moving Average, Moving Average, and the Naive Method, besides using Manual calculation, the approach of QM for windows is used as a comparison. The result will be determined by the six forecasting methods which is used as a proper basis for the next calculating based on the smallest MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) approach. The data in this study were made by the writer alone. 

Comparison of some annual forest inventory estimators

2002 ◽  
Vol 32 (11) ◽  
pp. 1992-1995 ◽  
Author(s):  
Paul C Van Deusen
Keyword(s):  
Forest Inventory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Current Status ◽  
Quadratic Growth ◽  
Growth Trends ◽  
Squared Error ◽  
Mixed Estimator ◽  
The One ◽  
Panel Design

Three estimators of current status and trend are compared for an annual interpenetrating panel design. The five-panel annual inventory design is simulated over a 10-year period with flat, increasing, and quadratic growth trends. The simulated comparisons show that the mixed estimator performs well relative to the 5-year moving average in terms of bias and mean squared error in all cases. The one-panel mean can have less bias than the moving average when there is a trend, but it is more variable. The moving average tends to lag evolving trends, which can result in very large bias.

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