scholarly journals Peramalan Penjualan Olein Curah di Perusahaan Pengolahan Kelapa Sawit Menggunakan Double Moving Average

2021 ◽  
Vol 2 (2) ◽  
pp. 39
Author(s):  
Nurike Oktavia ◽  
Alya Agustina ◽  
Ridha Luthvina
Keyword(s):  
Data Processing ◽  
Palm Oil ◽  
Moving Average ◽  
Percentage Error ◽  
Oil Processing ◽  
Cooking Oil ◽  
Forecasting Error ◽  
Forecasting Methods ◽  
Calculation Results ◽  
Oil Demand

Bulk olein is one of the products produced by Palm Oil Processing Company. Bulk cooking oil controls 75 percent of the production market share in Indonesia and about 77.5 percent of households in Indonesia use bulk cooking oil because the price is cheaper than packaged cooking oil. Demand for olein in the future is predicted to be continued to increase, so it is necessary to estimate future sales so that production activities become more effective and efficient. The method used in this study is the double moving average (DMA), which is one of the forecasting methods with data that has a trend. The calculation will be done by comparing the result of 3 moving, 4 moving and 5 moving. Forecasting error is calculated using mean absolute percentage error (MAPE). The calculation results show that the average MAPE from DMA with 5 moving has the smallest value. To verify these results, an analysis of the processed data was carried out, namely looking for data with the furthest distance from the linear line, namely t3 data and t7 data. The data is omitted in data processing and then the MAPE error value is recalculated. The results obtained are that DMA with 3 moving results have the smallest error, which is 11.863 percent. For this reason, the chosen forecasting calculation is a double moving average with 3 moving.

scholarly journals Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

2021 ◽  
Vol 2123 (1) ◽  
pp. 012044
Author(s):  
Sukarna ◽  
Elma Yulia Putri Ananda ◽  
Maya Sari Wahyuni
Keyword(s):  
Kalman Filter ◽  
Moving Average ◽  
Arima Model ◽  
Rainfall Data ◽  
Percentage Error ◽  
Filter Method ◽  
Rainfall Forecasting ◽  
Forecasting Methods ◽  
Filter Methods ◽  
Kalman Filter Method

Abstract Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.

scholarly journals Modelling the Employment in Tourism – Case Study of Croatia

2017 ◽  
Vol 8 (3) ◽  
pp. 37 ◽  
Author(s):  
Maja Mamula ◽  
Kristina Duvnjak
Keyword(s):  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Reliable Estimate ◽  
Monthly Data ◽  
Absolute Percentage Error ◽  
Forecasting Error ◽  
Forecasting Methods ◽  
Percentage Share

According to the data on the share of employees in the category Hotels and similar accommodation in the total employees (16.6% in 2015), it can be concluded that this percentage share is quite significant. In this paper the number of employees in tourism (in the category Hotels and similar accommodation) is modelled and predicted on the basis of monthly data from the period 2005 to 2015, collected from the First Release of the Croatian Bureau of Statistics. Taking into consideration the seasonal character of the phenomenon being analysed, taking into account the criteria of reliability of demonstrated forecasts, in this study following methods were used: the seasonal naive models, Holt - Winters Model trend seasonality exponential smoothing and Holt- Winters no seasonal exponential smoothing model. All obtained results were compared by forecasting error Mean Absolute Percentage error (MAPE). The obtained results indicate that forecasting methods which take into account the seasonal character of the phenomenon result in smaller forecasting error, and more reliable estimate, compared to models which don´t take into account the character of the phenomenon being analysed.

scholarly journals Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries

2020 ◽  
Author(s):  
Ghufran Ahmad ◽  
Furqan Ahmed ◽  
Suhail Rizwan ◽  
Javed Muhammad ◽  
Hira Fatima ◽  
...  
Keyword(s):  
Moving Average ◽  
Data Availability ◽  
Data Driven ◽  
Percentage Error ◽  
Pictorial Representation ◽  
Limited Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Methods ◽  
Global Pandemic ◽  
Future Burden

AbstractThe WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries, and has been declared as a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 173 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), random walk forecasts (RWF) with and without drift. We also evaluate the accuracy of these forecasts using the Mean Absolute Percentage Error (MAPE). The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generated heat maps to provide a pictorial representation of the countries at risk of having an increase in cases in the coming 4 weeks for June. Due to limited data availability during the ongoing pandemic, less data-hungry forecasting models like ARIMA and ETS can help in anticipating the future burden of SARS-CoV2 on healthcare systems.

scholarly journals The Effect of Spent Bleaching Earth Filler-Based NPK Fertilization on Proline, Growth and Yield of Maize

2019 ◽  
Vol 35 (1) ◽  
pp. 44
Author(s):  
Ranggi Sumanjaya Purba ◽  
Siti Nurul Rofiqo Irwan ◽  
Eka Tarwaca Susila Putra
Keyword(s):  
Palm Oil ◽  
Agricultural Sector ◽  
Block Design ◽  
Bleaching Earth ◽  
Growth And Yield ◽  
Oil Processing ◽  
Cooking Oil ◽  
Randomized Complete Block Design ◽  
Npk Fertilizer ◽  
Npk Fertilization

Spent bleaching earth (SBE) is by-product of cooking oil processing industry of crude palm oil (CPO). Palm oil industry is growing every year, followed by population growth and consumption of cooking oil so that the greater volume of waste generates SBE. An innovation is needed to anticipate the problem of SBE waste in agricultural sector, dealing with a filler component in the production of NPK fertilizer additives. This study aims to determine proline response, growth and yield of the maize to fertilization NPK with SBE-based filler. The experiments used Randomized Complete Block Design (RCBD) with the treatments of NPK filler (15:15:15) consisting of BC (brown clay), SBE and DBE (deoiled bleaching earth) at a dose of 6 g polybag<sup>-1</sup>. The results showed that the use of SBE gave the same effect on plant height, leaf number, stem diameter and 100-seed weight, but the use of SBE could increase 61.15% of proline activity. SBE can substitute filler on the additional materials of NPK fertilizer.

AN EXPONENTIALLY WEIGHTED MOVING AVERAGE METHOD WITH DESIGNED INPUT DATA ASSIGNMENTS FOR FORECASTING LIME PRICES IN THAILAND

2017 ◽  
Vol 79 (6) ◽  
Author(s):  
Thitima Booranawong ◽  
Apidet Booranawong
Keyword(s):  
Input Data ◽  
Moving Average ◽  
Weighting Factor ◽  
Percentage Error ◽  
Exponentially Weighted Moving Average ◽  
Monthly Data ◽  
Forecasting Error ◽  
The Mean ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In this paper, the Exponentially Weighted Moving Average (EWMA) method with designed input data assignments (i.e. the proposed method) is presented to forecast lime prices in Thailand during January 2016 to December 2016. The lime prices from January 2011 to December 2015 as the input data are gathered from the website’s database of Simummuang market, which is one of the big markets in Thailand. The novelty of our paper is that although the performance of the EWMA method significantly decreases when applying to forecast data which show trend and seasonality behaviors and the EWMA method is used for short-range forecasting (i.e. usually one month into the future), the proposed method can properly handle such mentioned problems. For this purpose, to forecast lime prices, five different input data are intently defined before assigned to the EWMA method: a) the monthly data of the year 2015 (i.e. the recent year data), b) the average monthly data of the year 2011 to 2015, c) the median of the monthly data of the year 2011 to 2015, d) the monthly data of the year 2011 to 2015 after applying the linear weighting factor, where the higher weight value is applied to the recent data, and e) the average monthly data of the year 2011 to 2015 after applying the exponential weighting factor, where the higher weight is also applied to the recent data. These designed input data are used as agents of the raw data. Our study reveals that using the input data b) with the EWMA method to forecast lime prices during January 2016 to September 2016 gives the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE). Forecasted lime prices of October 2016 to December 2016 are also provided. Additionally, we demonstrate that the proposed method works well compared with the Double Exponentially Weighted Moving Average (DEWMA), the Multiplicative Holt-Winters (MHW), and the Additive Holt-Winters (AHW) methods, which are suitably used for forecasting data with the trend and the seasonality.

scholarly journals Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study

10.2196/24925 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e24925 ◽  
Author(s):  
Christopher J Lynch ◽  
Ross Gore
Keyword(s):  
Early Onset ◽  
Short Range ◽  
Moving Average ◽  
New York Times ◽  
Health District ◽  
Percentage Error ◽  
Short Term ◽  
The Public ◽  
Forecasting Methods ◽  
Case Count

Background Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. Objective COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior–day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. Methods We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia’s 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. Results The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days’ counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. Conclusions For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

Engine Performance Investigations of Palm Oil, Jatropha Oil and Waste Cooking Oil as Alternative Fuel

2015 ◽  
Vol 1113 ◽  
pp. 674-678
Author(s):  
Syarifah Yunus ◽  
Noriah Yusoff ◽  
Muhammad Faiz Fikri Ahmad Khaidzir ◽  
Siti Khadijah Alias ◽  
Freddawati Rashiddy Wong ◽  
...  
Keyword(s):  
Palm Oil ◽  
Engine Performance ◽  
Alternative Fuel ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Gas Temperature ◽  
Performance Effect ◽  
Cooking Oil ◽  
Exhaust Gas Temperature ◽  
Global Agenda

The continued using of petroleum energy as a sourced for fuel is widely recognized as unsustainable because of the decreasing of supplies while increasing of the demand. Therefore, it becomes a global agenda to develop a renewable, sustainable and alternative fuel to meets with all the demand. Thus, biodiesel seems to be one of the best choices. In Malaysia, the biodiesel used is from edible vegetable oil sources; palm oil. The uses of palm oil as biodiesel production source have been concern because of the competition with food materials. In this study, various types of biodiesel feedstock are being studied and compared with diesel. The purpose of this comparison is to obtain the optimum engine performance of these different types of biodiesel (edible, non-edible, waste cooking oil) on which are more suitable to be used as alternative fuel. The optimum engine performance effect can be obtains by considering the Brake Power (BP), Specific Fuel Consumption (SFC), Exhaust Gas Temperature (EGT) and Brake Thermal Efficiency (BTE).

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

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