Holt Double Exponential Smoothing Research Articles

Supply Control Of Oil Filter Spareparts Using EOQ And Min-Max Methods Based On Sales Forecast (Case Study: PT Astra International Tbk Soekarno Hatta)

Planning and controlling product inventory is very important for a company to ensure demand is met and maximize profits from sales effectively and efficiently. In addition, inventory planning and control are carried out to prevent excess or shortage of inventory. This study aims to predict sales and calculate inventory control to overcome excess stock and increased inventory costs on oil filter spare parts. The forecasting method used is Holt's Double Exponential Smoothing method to determine the number of sales of the four types of oil filter spare parts in the future and overcome the problem of oil filter spare parts inventory using the EOQ and Min-Max methods. Based on the research results, the resulting forecasting is accurate and the EOQ method results in a lower total inventory cost than the Min-Max method.

Prediksi Ekspor Migas Indonesia Dengan Double Exponential Smoothing

In Indonesia, there are several forms of exports, one of which is oil and gas exports. The Double Exponential Smoothing method is a forecasting method that can be used to forecast oil and gas export data. This method uses two smoothing parameters, namely the average smoothing parameter alpha (α), the trend smoothing parameter beta (β). In this study, the population used is data on oil and gas exports in Indonesia. And the sample used is Indonesia's oil and gas export data from January 2020 to October 2021, we get 22 data sourced from the Central Statistics Agency (BPS). The Double Exponential Smoothing method is used because the data to be processed has a trend, both an up and a down trend and gas exports in Indonesia have an up and down pattern. The Double Exponential Smoothing method can also predict the prediction of oil and gas exports in Indonesia in the next months, November 2021 to February 2022. To process the data, the researcher used the Holt Double Exponential Smoothing method .

Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting

As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index.

Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods

Non-oil and gas exports could be forecasted using exponential smoothing for future periods. This study examines non-oil and gas export data in Indonesia from January 2015 to May 2021, indicating trends and seasonality. Based on the data characteristics, the obtained data were analyzed using Holt's double exponential smoothing method and triple exponential smoothing with multiplicative and additives Holt-Winters. The MAPE for all three models is less than 10%, indicating that the method is very good and could be used to forecast the next period. Using MAPE as a comparison, the best model for non-oil and gas exports is the additive Holt-Winters method triple exponential smoothing, which has the lowest MAPE of any model. The best method was employed to forecast data, making it possible for us to anticipate the pattern of non-oil and gas exports. This forecast data could be used as the basis for policymakers' decision-making. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.

Selection of the Best Forecasting Method at PT. Indaco Warna Dunia

PT. Indaco Warna Dunia is a decorative paint company in Indonesia that produces products under the brands Envi, Belazo, and Top Seal. Preliminary observations revealed that the forecasting method used by the company is ineffective and inaccurate. This inaccurate forecast result company’s problem in fulfilling the demand. This study aims to select the best forecasting method to improve forecast effectiveness and accuracy. The research was conducted at the Tarakan depot, and the products understudy were a fast-moving product category, specifically the Envi brand. Several forecasting methods such as Moving Average, Weighted Moving Average, Single Exponential Smoothing, Exponential Smoothing with Trend, and Holt's Double Exponential Smoothing. The accuracy of forecasting is the most important and it can be measured with MAPE (Mean Absolute Percentage Error). The results showed that Holt's Double Exponential Smoothing method is the best for three products, while the Exponential Smoothing with Trend method, and Single Exponential Smoothing method are the best for one of the products, respectively.

METODE DOUBLE EKSPONENSIAL SMOOTHING DALAM MEMPERKIRAKAN JUMLAH KEBUTUHAN ENERGI LISTRIK DI PT PLN (PERSERO) WILAYAH SUMUT

Electrical energy is one of the most important things in human life. Electrical energy is needed in the industrial sector. In meeting the needs of electrical energy, good planning is needed by predicting the needs of electrical energy. Holt's Double Exponential Smoothing method is a method that can be used to predict electrical energy needs. The results of forecasting the demand for electrical energy at PT PLN (Persero) for the North Sumatra Region for 2022 to 2030 use the Double Double Exponential Smoothing method from Holt (with a value of α = 0.99 and γ = 0.1 which has a MAPE value of 2.0372%. ) namely 13933.19 gwh, 14478.46 gwh, 15023.73 gwh, 15569.00 gwh, 16114.26 gwh, 16659.53 gwh, 17204.80 gwh, 17750.06 gwh, 18295.33 gwh.

Peramalan Harga Beras dengan Metode Double Exponential Smoothing dan Fuzzy Time Series (Study Kasus : Harga Beras di Kota Mataram)

Rice has become the main staple food for almost the entire population of Indonesia. However, in Indonesia, the price of food commodities (rice) often fluctuates in price. Due to the rapid fluctuation of rice prices and the uncertainty in the future, it is necessary to forecast rice prices. This study aims to predict the price of rice in the city of Mataram using the Holt double exponential smoothing method and the Cheng fuzzy time series. The model's performance is based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. Forecasting model based on Holt's double exponential smoothing method, the MSE value is 705967.4994 and the MAPE value is 7.91%. On the other hand, based on Cheng's fuzzy time series method, the performance of the forecasting model based on the MSE indicator is 566312.340 and based on the MAPE value of 6.75%. Based on these results, Cheng's fuzzy time series method is more accurate than Holt's double exponential smoothing method. Keywords: Double Exponential Smoothing Holt, Fuzzy Time Series Cheng, Rice Price, MAPE, MSE

A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

Comparative Study Between Holt's Double Exponential Smoothing and Fuzzy Time Series Markov Chain in Gold Price Forecasting

Gold price is important to a country’s economy as it can be used as a hedge against inflation especially during financial turmoil. Besides, the gold price also has an impact on the stock market price. As an investor, to make a good investment plan, information regarding the fluctuation price of gold is necessary to minimize the risk. Therefore, this study proposes to compare two of the forecasting models, namely Holt's Double Exponential Smoothing and Fuzzy Time Series Markov Chain to forecast the price of gold. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to determine a better forecasting model with smaller error. Initially, the data price of gold is analysed by using Durbin Watson Test to check the suitability of the data for time series analysis. The finding of this study shows that Fuzzy Time Series Markov Chain is more accurate in predicting gold price as compared to Holt’s Double Exponential Smoothing because it produces smaller values of RMSE and MAPE.

Estimation of the Open Unemployn Rate on Province Jambi Using the Double Exponential Smootthing

Unemployment is an employment problem that is often faced in every region, especially in Jambi Province. The indicator used to determine the number of unemployed is the open unemployment rate (TPT). High unemployment can be a major source of poverty that can lead to crime and hinder the process of economic development. As an initial precaution for the government before the increase in unemployment, which is to estimate the future TPT in Jambi Province. One of the methods used in estimating TPT in Jambi Province is the Double Exponential Smoothing Method from Holt. The measure of the forecast error uses the Mean Absolute Percent Error (MAPE). The aim is to obtain estimated TPT results in Jambi Province in the coming year, using Holt's Double Exponential Smoothing Method. The estimation results obtained, for TPT data there is a MAPE value of 18.96% with parameters = 0.9 and = 0.2. The estimation model obtained is = . The estimation results show that the TPT rate will continue to increase next year, it can be taken into consideration for the government in determining future plans to reduce TPT in order to accelerate economic development in Jambi Province.

Application of Double Exponential Smoothing Holt and Triple Exponential Smoothing Holt-Winter with Golden Section Optimization to Forecast Export Value of East Borneo Province

Exponential smoothing is one of the short term forecasting methods. The selection of the forecasting method can be done by considering the type of data pattern, such as the Double Exponential Smoothing (DES) Holt method which can be used on trend patterned data and the Triple Exponential Smoothing (TES) Holt-Winter method which can be used on trend and seasonal patterned data. The main problem in using the Holt DES and Holt-Winter TES methods is the parameter selection which is usually done by trial and error, but this method takes a long time so that in this research a more efficient method is used to obtain optimal parameters, namely the golden section method. The purpose of this research was to forecast and obtain the best method for forecasting the export value of East Borneo Province. The results showed that the forecasted of export values used the Holt DES, the additive Holt-Winter TES, and the multiplicative Holt-Winter TES with golden section optimization method had a MAPE of less than 10%, which means that the forecast used these methods were very good. The best method to predict the export value of East Borneo Province was the additive Holt-Winter TES with golden section optimization method.

Wind speed prediction for small sample dataset using hybrid first‐order accumulated generating operation‐based double exponential smoothing model

AbstractWind power generation has recently emerged in many countries. Therefore, the availability of long‐term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first‐order accumulated generating operation‐based double exponential smoothing (AGO‐HDES) model is proposed for very short‐term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO‐HDES model with other forecasting models. Results show that the AGO‐HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time.

Prediction of passenger train using fuzzy time series and percentage change methods

In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

Determining the Timing of Investment in Mutual Fund Products using the Double Exponential Smoothing Model

Purpose: As we know, COVID-19 affects the economy of most of the world, one of the investment media that continues to grow is mutual fund investment. This study aims to find the right time to invest by studying data in the previous period.
 Design/methodology/approach: In this study, we collect daily data on Net Asset Value (NAV) prices from January to September 2021, by analyzing calculations using the double exponential smoothing method to predict the time with the lowest price for investing.
 Findings: The results showed that this study was able to predict the right time to invest in mutual funds using the Holt Double Exponential Smoothing method using the actual smoothing data parameter of 0.9 (alpha) and the trend data smoothing parameter of 0.1 (beta) with produces a Mean Absolute Percentage Error (MAPE) value of 0.85343. The right time to invest in this research is determined on November 2, 2021 with an estimated net asset value (NAV) of 2348.644032.
 Paper type: Research paper
 Keywords: Mutual Fund, Investment Time, Investment Tool, Double Exponential Smoothing.

Business cycle prediction: Application of Markov chain to online crowdlending

AbstractUsing Markov chains can improve the forecasting of the state of the total loan amount for the next month (growth, stagnation, or decline), compared to traditional forecasting techniques, if each of the previous month's basic information is available. Traditional statistical techniques have high forecasting errors and low accuracy for predicting the loan amounts and are no better than the naïve method of “no change” if data cannot be rapidly actualized. With recent data available, some traditional statistical techniques work better than the naïve method, but Holt double exponential smoothing has a higher mean absolute percentage error (MAPE). For predicting “states,” some traditional statistical methods improve with the degree of actualization (naïve, simple exponential smoothing), Holt and ARIMA decrease in performance, and TBATS remains the same. Since Markovian chains are better than all traditional time series forecasting techniques, it raises the benchmark for evaluating value added by artificial intelligence techniques for forecasting.

Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

Prediction accuracy of sales surprise for inventory turnover

ABSTRACT Sales surprise, the ratio of actual sales to forecasted sales for a year, is a key determinant of firms’ inventory turnover. While the previous literature on empirical inventory management frequently uses Holt's double exponential smoothing method for obtaining future sales forecasts, this method suffers from measurement error. In contrast, we directly use the data on management forecasts publicised by listed companies in manufacturing and retail industries for the fiscal years 1997 through 2014, which are available in Japan, and evaluate the adequacy of the exponential smoothing sales forecasts. Our analysis reveals that sales surprise measured via both means positively relates to inventory turnover in the majority of industries, consistent with previous literature; however, the two variables imply discrepancies, particularly when economic conditions change.

Forecasting Patient Discharge Before Noon: A Comparison Between Holt's and Box-Jenkins' Models.

The uncertainty and ambiguity of not knowing how many patients will be discharged impact patient throughput in hospitals, causing concerns for responding to demand for admissions. Understanding the potential number of patients to be discharged can support caregivers, ability to concentrate on the range of interactions that patients require to ensure early discharge. Accurate forecasting of patients expected to be discharged by noon is beneficial in accommodating patients who need services and in achieving sustainable patient satisfaction. Models to predict patient discharge before noon (DBN) were formulated using Holt's double exponential smoothing and Box-Jenkins' methods with the aim of achieving minimal errors in each model. The models are applied to 24 months of weekly patient discharge historic data in a medical observation unit and a short-stay clinical unit of a health care hospital system located on the East Coast of United States. DBN prediction outcomes were more accurate when applying Box-Jenkins' method than Holt's method. Analysis revealed that the model of ARIMA(3,1,2) is most suitable for forecasting. Upon the outcomes of forecast error metrics, the study identifies the mean absolute percent error for the ARIMA model is 14%. Box-Jenkins forecasting performance is superior in predicting DBN with the least forecast error. Predicted values are significant to decision-making interventions aimed at taking new patients, improving quality patient care, and meeting patient throughput performance goals.

E-waste in Australia: Generation estimation and untapped material recovery and revenue potential

Abstract This study presents an estimation of electrical and electronic equipment (EEE) products put-on-market (PoM), electronic-waste (e-waste) generation and the stock of EEE products in Australia from the year 2000–2047, considering seven categories that encompass 51 different types of product. Holt's double-exponential smoothing and dynamic lifespans (using the Weibull distribution function) are applied to compute past and future PoM and in e-waste generation, respectively. With the estimation, it is found that EEE PoM was increased from 470 kilo tons (kt) to 2135 kt in a timeframe of 2000–2015. On the other hand, e-waste generation was 115 kt in the year 2000, which then increased to 485 kt in the year 2010. For the projected period (2018–2047), the annual average growth of e-waste generation will be around 3%. E-waste generation will increase, particularly for large household appliances (LHA), small household appliances (SHA) and consumer equipment (CE), in terms of weight. This study evaluates the potentially recoverable material and revenue potential of regulated products (e.g., computer, televisions and other IT peripherals currently considered under the national television and computer recycling scheme (NTCRS) and mobile phones) and e-waste generated outside of the current product coverage. The gap between revenue generation from regulated and non-regulated products is quantitatively assessed for the first time and reported in this paper. Sensitivity and uncertainty analysis performed by Monte Carlo simulation showed the robustness and accuracy of this study. This quantification will provide invaluable insights to policymakers, including products in future legislative reform as well as the development of the recycling industry in Australia. Furthermore, this study presents a transparent process of calculation for time-series data that can be used for e-waste generation estimation for other countries, as well.

Perbandingan Metode Holt Eksponential Smoothing dan Winter Eksponential Smoothing Untuk Peramalan Penjualan Souvenir

UD. Fajar Jaya is a trading business unit engaged in the supply of souvenirs. But in the management of the business there are some problems of which are UD. Fajar Jaya can not predict how the optimal number of souvenirs that must be provided to customers on every item souvenirs are sold. This causes the service to consumers less than the maximum, especially at certain moments sales of souvenirs (example: glass souvenirs) jumped dramatically from the number of average sales. To overcome the above, the authors propose to forecast the level of sales of souvenirs using Holt and Winter methods that exist in the development of Exponential Smoothing (ES) method. From the application of the two methods, then will make comparison of effectiveness of method which measured through actual data accuracy and forecasting result by knowing forecast error level. From the research results obtained forecasting results for Holt Double Exponential Smoothing method in July of 2017 is amounted to 599 items that may be sold with MAD forecasting error rate of 10.54 and MAPE of 3.70%. As for forecasting using Winter Exponential Smoothing method in July of 2017 is 549.6 items that may be sold with MAD 0.02 and MAPE error rate of 2.55%. The conclusion that can be drawn from the research that has been done on sales data souvenirs on UD. Fajar Jaya is that the Winter Exponential Smoothing method is more suitable to be applied in case study of souvenir sales in UD. Fajar Jaya is more than Holt Double Exponential Smoothing method.