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1
Andriani, Novita, Sri Wahyuningsih, and Meiliyani Siringoringo. "Application of Double Exponential Smoothing Holt and Triple Exponential Smoothing Holt-Winter with Golden Section Optimization to Forecast Export Value of East Borneo Province." Jurnal Matematika, Statistika dan Komputasi 18, no. 3 (May 15, 2022): 475–83. http://dx.doi.org/10.20956/j.v18i3.17492.
Анотація:
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.
2
Septiana, Dian. "Forecasting Rice Prices with Holt-Winter Exponential Smoothing Model." Hanif Journal of Information Systems 1, no. 2 (February 17, 2024): 62–67. http://dx.doi.org/10.56211/hanif.v1i2.17.
Анотація:
Rice, as a staple food, plays a crucial role in global food security. Accurate forecasting of rice prices is essential for policymakers, farmers, and consumers alike. This article explores the application of the Holt-Winter exponential smoothing model to predict rice prices. Holt-Winter method is chosen for its ability to capture both trend and seasonality in time series data, which are prominent features in agricultural commodity prices such as rice. The study analyzes historical price data, identifies trends, seasonality, and incorporates smoothing parameters in additive and multiplicative methods. Results indicate that additive method of Holt-Winter exponential smoothing provides a better performance. This research contributes valuable insights to the field of agricultural economics and informs strategies for managing food supply chains and market stability.
3
Jaber, Abobaker M., Mohd Tahir Ismail, and Alsaidi M. Altaher. "Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/708918.
Анотація:
This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
4
Utami, Ruli, and Suryo Atmojo. "Perbandingan Metode Holt Eksponential Smoothing dan Winter Eksponential Smoothing Untuk Peramalan Penjualan Souvenir." Jurnal Ilmiah Teknologi Informasi Asia 11, no. 2 (August 1, 2017): 123. http://dx.doi.org/10.32815/jitika.v11i2.191.
Анотація:
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.
5
Salamiah, Mia, Sukono Sukono, and Eddy Djauhari. "Prediction of the Number of Visitors to Tourism Objects in the Ujung Genteng Coastal Area of Sukabumi Using the Holt-Winter Method." Operations Research: International Conference Series 2, no. 4 (December 5, 2021): 109–16. http://dx.doi.org/10.47194/orics.v2i4.184.
Анотація:
Ujung Genteng Sukabumi Beach is one of the tourism destinations in Sukabumi Regency, West Java. Forecasting tourist arrivals is a very important factor for tourist destination policies and contributes to the regional economy and the surrounding community. The purpose of this study is to predict the number of tourists who come to Ujung Genteng Beach, Sukabumi. The method used is the Holt-Winter approach exponential smoothing. The Holt-Winter method is used for data that is not stationary, has both trend and seasonal elements. The Holt-Winters method has two models, namely the Additive model and the Multiplicative model. The data used is visitor data in January 2017 - February 2020, the results of the analysis show that the prediction of the number of visitors to Ujung Genteng beach in March 2020 from the additive model is 300 people with a MAPE value of 85.48% and an MSE value of 31230672.68 and a prediction of the number of beach visitors. Ujung Genteng in March 2020 from a multiplicative model of 740 people, with MAPE and MSE values obtained were 86.34% and 27754873.34.
6
Fauzi, Nur Fatihah, Nurul Shahiera Ahmadi, Nor Hayati Shafii, and Huda Zuhrah Ab Halim. "A Comparison Study on Fuzzy Time Series and Holt-Winter Model in Forecasting Tourist Arrival in Langkawi, Kedah." Journal of Computing Research and Innovation 5, no. 1 (October 2, 2020): 34–43. http://dx.doi.org/10.24191/jcrinn.v5i1.138.
Анотація:
The tourism industry in Malaysia has been growing significantly over the years. Tourism has been one of the major donors to Malaysia’s economy. Based on the report from the Department of Statistics, a total of domestic visitors in Malaysia were recorded at about 221.3 million in 2018 with an increase of 7.7% alongside a higher record in visitor arrivals and tourism expenditure. This study aims to make a comparison between two methods, which are Fuzzy Time Series and Holt-Winter in forecasting the number of tourist arrival in Langkawi based on the monthly tourist arrival data from January 2015 to December 2019. Both models were generated using Microsoft Excel in obtaining the forecast value. The Mean Square Error (MSE) has been calculated in this study to get the best model by looking at the lowest value. The result found that Holt-Winter has the lowest value that is 713524285 compared to the Fuzzy Time Series with a value of 2625517469. Thus, the Holt-Winter model is the best method and has been used to forecast the tourist arrival for the next 2 years. The forecast value for the years 2020 and 2021 are displayed by month.
7
Setiawan, Dwi, Eko Sediyono, and Irwan Sembiring. "Pemanfaatan Metode Association Rules dan Holt-Winter Multiplicative untuk Meningkatkan Peluang Penjualan Obat Pertanian." JURNAL SISTEM INFORMASI BISNIS 10, no. 1 (March 25, 2020): 46–55. http://dx.doi.org/10.21456/vol10iss1pp46-55.
Анотація:
The competition level between companies on executing product marketing is rapidly increasing, so the companies have to understand the importance of correlation between external environments of company with consumer’s needs. One of the efforts that can be done is by utilizing data warehouse and the application of infrastructure in information and technology field. This research combined Association Rules method to extracting pattern and finding every possibility that potential to increase sales and Holt-Winter Multiplicative method to estimate the alteration of trend on the seasonal data. After passed through data processing process by using RapidMiner tools, information that consists of correlation pattern between rule that describe the comparison of product and the sales working area and season that affects the product sale. The pattern used by company to know which product is often purchased by customer. Besides that, this research produces changing trend data of PT ABC’s product that generated by result of previous data comparison with forecast data. Based on value of error rate Mean Absolute Percentage Error (MAPE) in estimating forecast result on the PT ABC’s sales transaction data during 3 years, it shows good level of accuracy. Result of data test, by considering rule that formed and forecast result so the company can control and manage product in order to avoid incorrect sales. This thing will effect on repression of operational cost and PT ABC can identify available opportunities to increase sale of agricultural medicine.
8
Lê, Đức Đạo, and Linh Chi Phạm. "Forecasting market demand using ARIMA and Holt - Winter method: A case study on canned fruit production company." TẠP CHÍ KHOA HỌC TRƯỜNG ĐẠI HỌC QUỐC TẾ HỒNG BÀNG 4 (June 24, 2023): 1–8. http://dx.doi.org/10.59294/hiujs.vol.4.2023.380.
Анотація:
Consumer demand is an important factor in any business, especially in the food retail industry whose products are perishable and have a short life cycle. The daily demand for a food product is affected by external factors, such as seasonality, price reduction and holidays. To satisfy the stochastic demand, product characteristics vary with customer are required to be timely updated based on market dynamicity. According to previous research, to choose suitable forecasting model is the main concern of enterprises on demand management issue. Proper demand forecasting provides organization with valuable information regarding their prospective in their current market, allowing to make appropriated production portfolio. By applying ARIMA and Holt-Winter, this paper aims to forecast the canned fruit demand at a specific company to help them eliminate waste of lean related to production and distribution. Results are evaluated according to forecasting errors (MAD, MSE, MAPE). By comparing the aforementioned methods, it can be concluded that ARIMA outperforms Holt-Winter related to prediction accuracy.
9
Sucipto, Lalu, and Syaharuddin Syaharuddin. "Konstruksi Forecasting System Multi-Model untuk pemodelan matematika pada peramalan Indeks Pembangunan Manusia Provinsi Nusa Tenggara Barat." Register: Jurnal Ilmiah Teknologi Sistem Informasi 4, no. 2 (July 1, 2018): 114. http://dx.doi.org/10.26594/register.v4i2.1263.
Анотація:
Penelitian ini bertujuan untuk mengembangkan produk Forecasting System Multi-Model (FSM) guna menentukan metode terbaik dalam sistem peramalan (forecast) dengan mengkonstruksi beberapa metode dalam bentuk Graphical User Interface (GUI) Matlab dengan menghitung semua indikator tingkat akurasi guna menemukan model matematika terbaik dari data time series pada periode tertentu. Pada tahap simulasi, tim peneliti menggunakan data Indeks Pembangunan Manusia (IPM) Provinsi Nusa Tenggara Barat (NTB) tahun 2010-2017 guna memprediksi IPM NTB tahun 2018. Adapun metode yang diuji adalah Moving Average (SMA, WMA dan EMA), Exponential Smoothing Method (SES, Brown, Holt, dan Winter), Naive Method, Interpolation Method (Newton Gregory), dan Artificial Neural Network (Back Propagation). Kemudian model dievaluasi untuk melihat tingkat akurasi masing-masing metode berdasarkan nilai MAD, MSE, dan MAPE. Berdasarkan hasil simulasi data dari 10 metode yang diuji diketahui bahwa metode Holt paling akurat dengan hasil prediksi tahun 2018 sebesar 67,45 dengan MAD, MSE, dan MAPE berturut-turut sebesar 0,22654; 0,075955 dan 0,34829. The purpose of this research is to develop a product was called Forecasting System Multi-Model (FSM) to determine the best method in the forecasting system by constructing several methods in the form of Graphical User Interface (GUI) Matlab. It was done by all indicator accuration to find the best mathematical model of time series data in a certain period. In the simulation phase, this research used the Human Development Index (HDI) data of West Nusa Tenggara (NTB) Province in 2010 - 2017 to predict the HDI data of NTB in 2018. The methods tested were Moving Average (SMA, WMA and EMA), Exponential Smoothing Method (SES, Brown, Holt, and Winter), Naive Method, Interpolation Method (Newton Gregory), and Artificial Neural Network (Back Propagation). Then the models/methods were evaluated to see the level of accuracy of each method based on the value of MAD, MSE, and MAPE. Based on data simulation result from 10 tested method known that Holt method is most accurate with prediction result of 2018 equal to 67,45 with MAD, MSE, and MAPE respectively equal to 0.22654, 0.075955 and 0.34829.
10
Pertiwi, Dewi Darma. "Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City." Journal of Intelligent Computing and Health Informatics 1, no. 2 (September 30, 2020): 45. http://dx.doi.org/10.26714/jichi.v1i2.6330.
Анотація:
Weather conditions in the city of Mataram tend to be erratic and difficult to predict, such as the condition of rainfall data in 2018 which changes over a certain period of time so that the weather is difficult to predict accurately. In this study, we propose the Exponential Smoothing Holt-Winter method to forecast rainfall in the city of Mataram, so that it can be a decision support for various interested sectors. This method has been tested using secondary data from the Mataram City Central Bureau of Statistics for the period January 2014 to 2018 and evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results of this study indicate that using the Exponential Smoothing Holt-Winter method yields better results, each of which is MAPE 142.3, MAD 95.6 and MSD value 24988.7 and the data smoothing value is obtained for the smallest combination value of α 0.2, β 0.1, and γ 0.1. It can be concluded that the proposed method can provide better information and can be used to predict rainfall in Mataram City for the next 12 periods.
11
Zhao, Yangyuhui. "Research and Forecasting of the FTSE100 Index over Long Time Series." Advances in Economics, Management and Political Sciences 86, no. 1 (June 28, 2024): 133–42. http://dx.doi.org/10.54254/2754-1169/86/20240887.
Анотація:
In order to research the UK and Global stock market, FTSE100 index is one of the most important values to study. This paper uses several models to forecast the future curve of the FTSE100 and compare these models to find a best way on forecasting. Then by analyzing the timeseries, the author shows several factors that might affect the timeseries which will be useful on the further forecast of the market. By using the simple forecast method, including Mean, Nave, Snaive and Drift, Holt and Holt-winter model, and ARIMA model on the closing price of FTSE100 from Feb.2004 to Feb.2024. The result show that Holt-winter is the best model, and the mean is the worst. Also, by considering the 100 companies of the FTSE100 the factors that will affect the time series are Energy Boom, Global Economy, and Foreign Exchange Market. Therefore, the article might give investors some idea about the UK stock market.
12
Nurdini, Arief, and Ardhy Lazuardy. "ANALYSIS OF DEMAND FORECASTING FOR TEMPEH PRODUCTS AT INDONESIAN TEMPEH HOUSES USING THE HOLT-WINTERS ADDITIVE METHOD APPROACH." International Journal Science and Technology 2, no. 1 (March 30, 2023): 59–64. http://dx.doi.org/10.56127/ijst.v2i1.854.
Анотація:
Rumah Tempe Indonesia is an MSME engaged in processing soybeans into tempeh products. The production system used is made to stockThis production system can cause problems, including the amount of production that does not match consumer needs, causing a shortage or excess of products which are very inefficient for the company's business continuity. For this reason, a study was carried out to determine the forecast for the demand for GMO Tempe at Indonesian Tempe Houses for the next 12 periods using the Holt-winters method and to determine the accuracy of the forecast made. The method used in this research is the Holt-winter method with the help of Ms. Excel Where. The final result of the research using Holt-winters has a level of forecasting accuracy90.1515344%, which means it is very good at predicting the demand for tempe in the future. Forecasting results in periods 37 to 48 respectively are 13372PCS, 12367PCS, 14196PCS, 12848PCS, 16655PCS, 15965PCS, 18032PCS, 15107PCS, 15132PCS, 17969PCS, 14267PCS, 21498PCS.
13
Rani Reddy, Dr M. "Forecasting Railway Passengers Demand Using Holt-Winter Method With R Statistical Tool." International Journal of Advanced Multidisciplinary Scientific Research 2, no. 8 (August 31, 2020): 1–8. http://dx.doi.org/10.31426/ijamsr.2019.2.8.1811.
14
Firmanto, Devit Hari, Eko Prasetyo, and Mas Nurul Hamidah. "Instant Cement Forming Using Holt-Winter (case Study: CV Trijaya Abadi)." JEECS (Journal of Electrical Engineering and Computer Sciences) 3, no. 1 (June 29, 2018): 389–94. http://dx.doi.org/10.54732/jeecs.v3i1.145.
Анотація:
CV.Trijaya Abadi is an industry that produces cement, and make various innovations by producing instant cement. Itis often the case with errors in doing the forecasting is if the amount of production is produced too much while thedemand is small it will cause losses for the company as well as vice versa if the demand a lot while the productionwill be a bit disappointment of consumers resulting in the company losing konsakuya. know the amount of instantcement production in the next period. The method used for forecasting in this research is Exponential SmoothingHolt-Winters method with multiplyative seasonal method and additive seasonal method. The alpha, beta and gammavalues used are 0.9, 0.1, and 0.1. With the value of these parameters are able to produce the value of MSEamounting to 52347.63 and MAPE value of 6,649 is forecasting in 2016 for multiplyative seasonal method. Foradditive seasonal method, the value of MSE is 50560.88 and MAPE value of 6,619 forecasting in 2016. So it isconcluded that it is more accurate to use the Holt-Winters additive seasonal method in 2016 forecasting of instantcement.
15
Aribowo, Anung B., Dedy Sugiarto, Iveline Anne Marie, and Jeany Fadhilah Agatha Siahaan. "Peramalan harga beras IR64 kualitas III menggunakan metode Multi Layer Perceptron, Holt-Winters dan Auto Regressive Integrated Moving Average." Ultimatics : Jurnal Teknik Informatika 11, no. 2 (January 16, 2020): 60–64. http://dx.doi.org/10.31937/ti.v11i2.1246.
Анотація:
This paper aims to present the analysis of price movements of IR64 quality III at the Cipinang Rice Main Market (PIBC) and the accuracy comparison of forecasting using Multi Layer Perceptron (MLP), Holt-Winters, and Auto Reggressive Integrated Moving Average (ARIMA) method. The data are daily price from 1 January 2016 to 31 May 2018 sourced from PT. Food Station. The analysis shows that the price of IR64 quality III rice tends to rise towards the end of 2016 and 2017. This is related to the decrease in the level of rice supply by January each year which encourages PT Food Station to conduct market operations to control the price of rice in the market. The results of accuracy comparison show that the MLP produces a value of Root Mean Square Error (RMSE) of 5,67, Holt-Winters exponential smoothing with trend and additive seasonal component produces a value RMSE of 70.71 and ARIMA method with parameters (1,1,2) resulted in RMSE values of 58.71. The RMSE values of the MLP method have smaller values than the Holt Winter and ARIMA methods which indicate that the MLP method is more accurate
16
Andayani, Puji. "Implementation of Holt-Winter Exponential Smoothing Method to Forecast The Spread of Covid-19." Indonesian Journal of Mathematics and Applications 1, no. 2 (September 30, 2023): 13–24. http://dx.doi.org/10.21776/ub.ijma.2023.001.02.2.
Анотація:
This research examines the Holt winters exponential smoothing method to solve forecasting problems using case studies of the spread of Covid-19. The data source uses the transmission of Covid-19 in Indonesia. MAPE is used to measure errors in data forecasting. The results are structured to serve as a recommendation for other researchers in choosing a method for predicting the spread of the disease. Based on the results, forecasting with the Holt-Winters model in positive cases produces a MAPE value of 9.21% using the Multiplicative model and the best parameter values of alpha, beta and gamma (0.4, 0.05, 0.25). Whereas in the case of human recovery, the MAPE value was 11.86% using the Multiplicative model and the best values for the parameters alpha, beta and gamma (0.4, 0.03, 0.1). And in the case of death it produces a MAPE of 17.97% using the Multiplicative model and the parameter values alpha, beta and gamma (0.4, 0.01, 0.1). So, it can be concluded that the Holt-Winters method on human positive case data shows a good outcome performance while recovered and deceased cases produce a well-being analysis performance.
17
Ade Onny Siagian. "Struktur Peramalan System Multi-Model untuk pemodelan matematika pada Forecast Indeks Pembangunan Manusia Provinsi Bali." DIAJAR: Jurnal Pendidikan dan Pembelajaran 1, no. 1 (January 20, 2022): 86–94. http://dx.doi.org/10.54259/diajar.v1i1.204.
Анотація:
The purpose of this research is to develop a product was called Forecasting System Multi-Model (FSM) to determine the best method in the forecasting system by constructing several methods in the form of Graphical User Interface (GUI) Matlab. It was done by all indicator accuration to find the best mathematical model of time series data in a certain period. In the simulation phase, this research used the Human Development Index (HDI) data of Bali Province in 2010 - 2017 to predict the HDI data of Bali in 2018. The methods tested were Moving Average (SMA, WMA and EMA), Exponential Smoothing Method (SES, Brown, Holt, and Winter), Naive Method, Interpolation Method (Newton Gregory), and Artificial Neural Network (Back Propagation). Then the models/methods were evaluated to see the level of accuracy of each method based on the value of MAD, MSE, and MAPE. Based on data simulation result from 10 tested method known that Holt method is most accurate with prediction result of 2018 equal to 67,45 with MAD, MSE, and MAPE respectively equal to 0.22654, 0.075955 and 0.34829.
18
Rahman, Abdul, Dyah Alfa Sa'adah Al-adawiyyah, Muli ana, Syil Viya Rivika, Arisman Adnan, and Rado Yendra. "Holt-Winter Forecasting Method for Inflow and Outflow of Bank Indonesia in Riau." International Journal of Economics and Management Studies 8, no. 7 (July 25, 2021): 71–76. http://dx.doi.org/10.14445/23939125/ijems-v8i7p108.
19
Elmunim, Nouf Abd, Mardina Abdullah, Alina Hasbi, and Siti Aminah Bahari. "Investigation on the Implementation of the Holt-Winter Method for Ionospheric Delay Forecasting." Advanced Science Letters 23, no. 2 (February 1, 2017): 1325–28. http://dx.doi.org/10.1166/asl.2017.8356.
20
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.
Анотація:
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.
21
Andi Bimantoro, Fanji, Sugiyono Madelan, and Ahmad Badawi Saluy. "Forecasting With Time Series Method at PT. RSM in Bekasi Jawa Barat." Dinasti International Journal of Economics, Finance & Accounting 2, no. 3 (July 6, 2021): 273–82. http://dx.doi.org/10.38035/dijefa.v2i3.858.
Анотація:
This study aims to determine the most valid forecasting method based on the time series method. This research uses a quantitative descriptive method, the research variable is sales data of MT products belonging to PT. RSM period August 2018 to January 2021. Data processing using Microsoft excel and Minitab 19 software. ABC analysis results show product codes RSM020, RSM021, and RSM017 occupy the three highest ranks in class A by contributing 26.16% sales figures. Based on the forecasting results using various time series methods (linear trend, decomposition, moving average, single exponential smoothing, Holt Method, and Winter Method) it is found that the Winter Method produces the lowest MAPE value, which is below 20%. Product code RSM020 with an alpha value of 0.06; beta 0.09; and 0.07 gamma produces 17.2% MAPE. Product code RSM021 with an alpha value of 0.01; beta 0.01; and 0.01 gamma produces a 15.3% MAPE. Product code RSM017 with an alpha value of 0.01; beta 0.02; and 0.02 gamma produces 18.1% MAPE.
22
Diksa, I. Gusti Bagus Ngurah. "Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach." Jurnal Matematika, Statistika dan Komputasi 18, no. 2 (January 1, 2022): 237–50. http://dx.doi.org/10.20956/j.v18i2.18542.
Анотація:
Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).
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Ponziani, Regi Muzio. "Foreign Tourists Arrival Forecasting at Major Airports in Indonesia:." IJEBD (International Journal of Entrepreneurship and Business Development) 4, no. 5 (September 30, 2021): 662–70. http://dx.doi.org/10.29138/ijebd.v4i5.1507.
Анотація:
Purpose: This research purports to forecast the number of foreign tourists arriving at major airport in Indonesia. The airports chosen are Soekarno Hatta, Juanda, I Gusti Ngurah Rai, and Kualanamu international airports.
Design/methodology/approach: The data used were foreign tourists arrival at major airports located in Jakarta, Surabaya, Medan, and Denpasar. The data extended from January 2014 until December 2018. Two time-series methods were employed, namely Holt-Winter Seasonality and Exponential Smoothing with maximum likelihood. The forecasts would reveal the fitted numbers of foreign tourists arriving from January 2019 until December 2019. The fitted numbers would then be compared to the actual numbers of January 2019 to December 2019.
Findings: The results showed that, overall, Holt-Winters seasonality excel at forecasting foreign tourists arrival at Soekarno Hatta and Juanda international airports. While Exponential Smoothing perform better for prediction at I Gusti Ngurah Rai and Kualanamu international airports. The MAPE for Holt-Winters at Soekarno Hatta and Juanda international airports were 26.1585% and 14.538%. The MAPE for Exponential Smoothing at at I Gusti Ngurah Rai and Kualanamu international airports were 7.76% and 15.6791%.
Research limitations/implications: Forecasting for foreign tourist arrival at Soekarno Hatta and Juanda international airports should employ Holt-Winters approach. Forecasting for foreign tourists arrival at I Gusti Ngurah Rai and Kualanamu international airports should employ Exponential Smoothing with maximum likelihood.
Practical implications: Certain forecasting methods work better than the others at certain international airports. Many forercasting methods are available. Two methods are specifically prominent for detecting seasonality and trend, i.e Holt-Winters and Exponential Smoothing with maximum likelihood.
Originality/value: Most research focus on one method at a time. This research compares two methods so that we can know better which method is suitable for certain airports. Four international airports are sampled in this study. Not many research focus on several places at a time.
Paper type: Research paper
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Bayu, Gede Eridya, I. Ketut Gede Darma Putra, and Ni Kadek Dwi Rusjayanthi. "A Comparison Between Backpropagation, Holt-Winter, and Polynomial Regression Methods in Forecasting Dog Bites Cases in Bali." Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) 9, no. 3 (October 4, 2021): 251. http://dx.doi.org/10.24843/jim.2021.v09.i03.p06.
Анотація:
Rabies is a zoonotic disease that is usually transmitted to humans through animal bites. It can cause severe damage to the central nervous system and is generally fatal. Dog bite cases are considered the leading cause of rabies transmission in Bali. The government's preventive action is expected to reduce the problem of increasing the number of dog bite cases so that it does not spread quickly and cause casualties. Data mining is an attempt to extract knowledge from a set of data. The use of data mining in this study is to forecast the number of dog bite cases in Bali. Forecasting predicts what will happen in the future based on relevant data in the past and placing it in a mathematical model. Data mining methods used in forecasting dog bite cases are backpropagation, holt-winters, polynomial regression methods. This forecasting aims to help the government predict dog bite cases in the coming period to prepare appropriate countermeasures. Forecasting is done using data on bite cases every month in Bali province for five years, from 2015 to 2019. Dog bite case data is divided into four datasets for each attribute, namely data on the number of dog bite cases, the number of vaccinations, the number of male deaths, the number of female deaths. The four datasets are divided into training data and testing data to share 80% training data and 20% testing data. The results obtained are that the backpropagation method is better at predicting dog bite case data with an average MAPE error rate of 11.59%, while the holt-winters method has an average MAPE error rate of 16.05%, and the polynomial regression method has an average MAPE error rate of 19.91%
Keywords : Dog Bites, Rabies, Forecasting, Backpropagation, Holt-Winter, Polynomial Regression
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Rosita, Yesy Diah, and Lady Silk Moonlight. "Perbandingan Metode Prediksi untuk Nilai Jual USD: Holt-Winters, Holt's, dan Single Exponential Smoothing." JTIM : Jurnal Teknologi Informasi dan Multimedia 5, no. 4 (January 29, 2024): 322–33. http://dx.doi.org/10.35746/jtim.v5i4.473.
Анотація:
In the ever-changing landscape of the global economy, the role of the United States Dollar (USD) as the backbone of the international financial system significantly influences market stability and dynamics. The close correlation between fluctuations in the USD exchange rate and internal and external factors demands effective prediction methods to understand and manage associated risks. This study aims to compare the performance of three main prediction methods: Single Exponential Smoothing (SES), Holt's Method, and Holt-Winters Method, in forecasting USD exchange rates. Utilizing historical data from the Central Statistics Agency (BPS) and testing under three training data distribution scenarios (45%, 55%, and 75%), this research provides in-depth findings on the strengths and weaknesses of each prediction method. Performance evaluations include the time required, Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), R-Squared, and correlation for the implementation of each method. If averaged, the results are as follows for SES, Holt’s, and Holt’s Winter, respectively: SES (1.58; 284.20; 68,768.26; 440.07; 0.03; -2.12; Nan), Holt’s (1.39; 890.23; 426,377.44; 1,043.28; 0.06; -24.28; -0.66), and Holt’s Winter (1.20; 997.45; 513,657.58; 1,168.00; 0.07; -30.62; -1.55). Overall, this indicates that the Holt-Winters Method stands out with significant performance, especially in scenarios with larger training data distributions, with a low R-Squared value (-4.618) and satisfactory correlation (0.417). Holt's Method also shows improved accuracy, while Single Exponential Smoothing (SES) offers time efficiency, albeit with limitations in explaining data variations. In conclusion, this research provides valuable guidance for business stakeholders, investors, and policymakers in selecting prediction methods suitable for their data characteristics and analysis goals, with the potential for a positive impact on business strategies, competitiveness, and risk management amid the uncertainty of USD exchange rate fluctuations.
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Aini, Novi Nur, Atiek Iriany, Waego Hadi Nugroho, and Faddli Lindra Wibowo. "Comparison of Adaptive Holt-Winters Exponential Smoothing and Recurrent Neural Network Model for Forecasting Rainfall in Malang City." ComTech: Computer, Mathematics and Engineering Applications 13, no. 2 (November 23, 2022): 87–96. http://dx.doi.org/10.21512/comtech.v13i2.7570.
Анотація:
Rainfall forecast is necessary for many aspects of regional management. Prediction of rainfall is useful for reducing negative impacts caused by the intensity of rainfall, such as landslides, floods, and storms. Hence, a rainfall forecast with good accuracy is needed. Many rainfall forecasting models have been developed, including the adaptive Holt-Winters exponential smoothing method and the Recurrent Neural Network (RNN) method. The research aimed to compare the result of forecasting between the Holt-Winters adaptive exponential smoothing method and the Recurrent Neural Network (RNN) method. The data were monthly rainfall data in Malang City from January 1983 to December 2019 obtained from a website. Then, the data were divided into training data and testing data. Training data consisted of rainfall data in Malang City from January 1983 to December 2017. Meanwhile, the testing data were rainfall data in Malang City from January 2018 to December 2019. The comparison result was assessed based on the values of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result reveals that the RNN method has better RMSE and MAPE values, namely RMSE values of 0,377 and MAPE values of 1,596, than the Holt-Winter Adaptive Exponential Smoothing method with RMSE values of 0,500 and MAPE values of 0,620. It can be concluded that the non-linear model has better forecasting than the linear model. Therefore, the RNN model can be used in modeling and forecasting trend and seasonal time series.
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Sato, Enos Nobuo, Carlos Teixeira, Beck Nader, and Giorgio de Tomi. "Time Series Models to Obtain the Barrel Crude Oil Prices." Materials Science Forum 805 (September 2014): 422–28. http://dx.doi.org/10.4028/www.scientific.net/msf.805.422.
Анотація:
The use of time series as an additional tool in decision making for the oil industry has been established as a mechanism for predicting the behavior of crude oil price. Especially in Brazil, after the discovery in this decade of the pre-salt reservoirs, the estimate of the price of a crude oil barrel through the use of modern techniques can minimize risks in exploration and production of oil. The more appropriate pricing for crude oil aims to minimize the risks to the economic activity for both exporters and importers of oil. This paper presents six different methods for obtaining crude oil future pricesi.e.Multiple regression (MR), Holt ́s method (HM), Holt-Winter (HW), Kalman filter (KF), Auto-Regression/Moving-Average (ARIMA) and stochastic simulation based on the use of the Monte Carlo method (SMC). The methods are compared to determine their advantages and disadvantages against each other, seeking to determine which of the generated models has the best potential to determine the future fair price of a barrel of oil. As a result, the most appropriate methodology capable of projecting a more precise future barrel oil fair price was determined, among the six alternatives studied.
28
Elmunim, N. A., M. Abdullah, A. M. Hasbi, and S. A. Bahari. "Comparison of GPS TEC variations with Holt-Winter method and IRI-2012 over Langkawi, Malaysia." Advances in Space Research 60, no. 2 (July 2017): 276–85. http://dx.doi.org/10.1016/j.asr.2016.07.025.
29
Pamungkas, A., R. Puspasari, A. Nurfiarini, R. Zulkarnain, and W. Waryanto. "Comparison of Exponential Smoothing Methods for Forecasting Marine Fish Production in Pekalongan Waters, Central Java." IOP Conference Series: Earth and Environmental Science 934, no. 1 (November 1, 2021): 012016. http://dx.doi.org/10.1088/1755-1315/934/1/012016.
Анотація:
Abstract Pekalongan waters, a part of the Java Sea, has potency to develop marine fisheries sector to increase regional income and community livelihoods. The fluctuation of marine fish production every year requires serious attention in planning and policy strategies for the utilization of the fishery resources. Time series fish production data can be used to predict fish production in the following years through the forecasting process. The data used in this study is fish production data from Pekalongan Fishing Port, Central Java, from January 2011 to December 2020. The method used is data exponential smoothing by comparing three exponential smoothing methods consisting of single/simple exponential smoothing, double exponential smoothing and Holt-Winters’ exponential smoothing. The criterion that used to measure the forecasting performance is the mean absolute percentage error (MAPE) value. The smaller MAPE value shows the better the forecasting result. The smallest MAPE value is obtained by finding the optimal smoothing constant value which is usually calculated using the trial and error method. However, in this study, the constant value was calculated using the add-in solver approach in Microsoft Excel. The forecasting results obtained show that forecasting using the Holt Winter Exponential Smoothing method is reasonable with a MAPE value of 37.878.
30
Shaleh, W., Rasim, and Wahyudin. "The System of Inventory Forecasting in PT. XYZ by using the Method of Holt Winter Multiplicative." IOP Conference Series: Materials Science and Engineering 288 (January 2018): 012152. http://dx.doi.org/10.1088/1757-899x/288/1/012152.
31
Tasia, Ena, Nanda Nazira, Qurotul A’yuniyah, M. Hayatul Fikri, and Andri Nofiar Am. "Analisis Model Manajemen Permintaan SCM dan Peramalan Penjualan Busana Menggunakan Metode Holt-Winter Exponential Smoothing." Jurnal Teknik Industri Terintegrasi 6, no. 4 (October 30, 2023): 1303–12. http://dx.doi.org/10.31004/jutin.v6i4.20313.
Анотація:
Sales forecasting is a highly crucial strategy in the business world, as it significantly contributes to enhancing a company's profits. In this context, sales transaction forecasting plays a vital role in assisting business decision-makers in planning effective sales strategies. The utilization of the Holt-Winters Exponential Smoothing method in sales forecasting demonstrates an effective approach. In this study, this method was applied to retail sales data of Muslim clothing from 2021 to 2023. By setting the parameters ? = 0.9, ? = 0.1, and ? = 0.1, the forecasting results indicate a high level of accuracy with low values for Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), amounting to 29.93, 295.93, and 0.62%, respectively. Consequently, the forecast reveals that the inventory of clothing for periods 13 to 16 is 83, 228, 129, and 115, respectively
32
Aziz, Rahmah. "Peramalan Jumlah Penumpang di Bandar Udara Soekarno-Hatta dengan menggunakan Pemulusan Eksponensial Tripel tipe Holt-Winter dan tipe Brown." Journal of Mathematics UNP 7, no. 3 (September 27, 2022): 63. http://dx.doi.org/10.24036/unpjomath.v7i3.12524.
Анотація:
The main airports in Indonesia, including at Soekarno-Hatta, are deserted, due to government policies to overcome the Covid-19 disease outbreak. The number of passengers has fallen drastically, every month the plane is erratic in carrying passengers. With a pandemic like this, the airline must make a strategy to avoid losses. To overcome this problem, the purpose of this study is to predict the number of passengers at Soekarno-Hatta Airport during the pandemic using triple exponential smoothing of Brown type and Holt-Winter type. In the triple exponential smoothing method, trend analysis is carried out and followed by smoothing three times.
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Jasman, Hapiz, Eman Lesmana, and Julita Nahar. "Forecasting Of Production And Export Indonesian Pepper Commodities Using Smoothing Exponential And Holt Winter Methods." IJEBD (International Journal of Entrepreneurship and Business Development) 4, no. 2 (April 1, 2021): 175–82. http://dx.doi.org/10.29138/ijebd.v4i2.1368.
Анотація:
Purpose: The last few years the contribution of Indonesian pepper in the world market has decreased and has been replaced by Vietnam. If in 2000 and a few years before Indonesia became the world’s main pepper exporter, since 2001 the position has been replaced by Vietnam. In 2006 Indonesia’s position fell back to number three the world was replaced by Brazil which was ranked second. In 2006 Indonesian exports outperformed brazil and returned to rank second. Based on data from the Directorate General of Plantations in 2015, the area under pepper in Indonesia tends to decrease from 2004 to 2015 with an average reduction of area of 3064.5 hectares per year. Based on data from the Directorate General of Plantation in 2015, the area of pepper in Indonesia tends to decline from 2004-2015 with an average reduction of 3,064.5 hectares per year. The occurrence of the deduction according to the Ministry of Agriculture (2013), among others, is caused by: (a) drought; (b) Pest and disease attacks, especially stem rot and jaundice; and (c) conversion of pepper into mining or other plantation land, such as oil palm, rubber or cocoa.
Design/methodology/approach:. Methods used to predict the number of production and consumption of domestic and export of Indonesian pepper is Double exponential Smoothing Brown and the Smoothing exponential method of Holt-Winter.
Research limitations/implications: This Paper discusses the predictions of production and domestic consumption and the export of Indonesian pepper.
34
Rachmadan, Muhammad Rizki. "Comparison of Multi Layer Perceptron and Holt Winter Accuracy in Forecasting Suzuki Car Brand Production in Indonesia." Operations Excellence: Journal of Applied Industrial Engineering 15, no. 1 (August 5, 2023): 89. http://dx.doi.org/10.22441/oe.2023.v15.i1.075.
Анотація:
Car production based on brand holding agents (APM) reports production value to the Indonesian Association of Automotive Industries (Gaikindo) in 2021 of 863,348 units with a percentage range of 11.4% by the Suzuki brand. Based on the public's interest in the need for a car which is the preferred option, the range and selling price are part of the considerations in determining the product of choice as the vehicle owner. This choice is important in activities to meet daily transportation needs. The purpose of this study is to obtain the most effective method and to maintain the number of vehicle unit production, production forecasts are needed according to people's purchasing power patterns using monthly data, especially the Suzuki brand. The research uses the Holt Winters measurement method with two hidden layers on the Multi Layer Perceptron (MLP) measurement method. The findings from this study are comparisons that can be said to be valid and can be an option in predicting data by utilizing methods to predict the amount of production by brand-holding agents. These results can contribute to optimizing the number of production results so that there are no excess or shortage of unit stock. The results showed that forecasting using a Multi Layer Perceptron with two hidden layers produced an accurate value where the lowest value at the Root Mean Square Error (RMSE) was 889.851 and the Mean Absolute Percentage Error (MAPE) was 9.3368.
35
RAIHANAH, RAIHANAH, ANITA TRISKA, and NURSANTI ANGGRIANI. "PERAMALAN JUMLAH KEDATANGAN WISATAWAN ASING BANDARA DI BALI DAN BANTEN MENGGUNAKAN METODE HOLT-WINTER ADITIF DAN MULTIPLIKATIF." E-Jurnal Matematika 12, no. 4 (November 30, 2023): 260. http://dx.doi.org/10.24843/mtk.2023.v12.i04.p427.
Анотація:
Tourism is an important sector in the Indonesian economy. One of the benchmarks for the development of the tourism sector is the number of foreign tourist arrivals to Indonesia. Forecasting the number of foreign tourist arrivals is needed so that actors contributing to the tourism sector can optimize their service efforts. It is necessary to forecast the number of foreign tourist arrivals, especially through the arrival gate at I Gusti Ngurah Rai airport (Bali) and Soekarno-Hatta airport (Banten) as one of the main arrival gates most visited by foreign tourists. This study aims to predict the number of foreign tourist arrivals through the airport by comparing the accuracy of the Holt-Winter's additive and multiplicative method. MAPE (Mean Absolute Percentage Error) and the Durbin Watson statistical test are used and to measure the accuracy of the forecast value against the original data. Overall, the MAPE value and Durbin Watson statistical test result indicate that the additive and multiplicative approaches are good enough to be used. However, judging from the smallest MAPE value, Holt-Winter multiplicative is better used in processing data on the number of foreign tourist arrivals at both I Gusti Ngurah Rai and Soekarno-Hatta airports with MAPE values ??of 7.57% and 6.80% respectively.
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Supriatna, A., E. Lesmana, L. Aridin, Sukono, and H. Napitupulu. "Comparison between multiplicative Holt Winter and decomposition method in predicting the number of incoming international tourists to Indonesia." IOP Conference Series: Materials Science and Engineering 567 (August 15, 2019): 012047. http://dx.doi.org/10.1088/1757-899x/567/1/012047.
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Al-Asadi, Afif Nuzia, Eko Prasetyo, and Rifki Fahrial Zainal. "Forecasting the Number of Brick Production Using the Method of Exponential Smoothing Holt-Winter (case Study: PT Sik Krian)." JEECS (Journal of Electrical Engineering and Computer Sciences) 1, no. 2 (December 30, 2016): 161–67. http://dx.doi.org/10.54732/jeecs.v1i2.178.
Анотація:
PT. SIK is an industry that produces a light brick type of brick. At a certain period, some companies are rising and the decline in demand which is quite significant. This research aims to know the condition of the company to overcome the overstock in the warehouse. The methods used to conduct forecasting in this research is a method of Exponential Smoothing Holt-Winter with seasonal multiplicative component and the addition of seasonal. The value of alpha, beta and gamma used is 0.6, 0.1, and0.5. With the value of the parameter is capable of producing the best MSE values with the value 1 in forecasting the year 2011 in October for seasonal multiplicative component, and the value of 0.006 in MAPE and the same month. For the addition of a seasonal best MSE values obtained on forecasting in 2013 in February with the value and worth of 5.016 MSE MAPE 0.013. The results of this research, the company was able to reduce the buildup of inventory and maximizing production for the coming period without having to fear a shortage of stock and overstocking.
38
Siswono, Galuh Oktavia, Yeni April Lina, and Verencia Pricila. "The Application of the Long-Short Term Memory (LSTM) Forecasting Method on the Impact of Tropical Cyclones in Indonesia." Jurnal Matematika, Statistika dan Komputasi 20, no. 1 (September 6, 2023): 294–300. http://dx.doi.org/10.20956/j.v20i1.27151.
Анотація:
Effective disaster mitigation strategies are paramount in the realm of risk management concerning natural calamities, with the primary objective of mitigating potential devastation. A pragmatic and impactful method involves predicting the contributory aspects of such disasters, encompassing variables such as torrential rainfall and formidable wind velocities that tropical cyclones bring. In this study, a comparative analysis of forecasting methodologies is undertaken, precisely the Long Short-Term Memory (LSTM) technique and the Holt Winter approach, both directed toward gauging the impact of tropical cyclones. This investigation focuses on two critical factors: the forecast of precipitation intensity and the estimation of maximum wind speed. The outcomes underscore the superior predictive capabilities of the LSTM method, unequivocally revealing its aptitude for predicting rainfall and wind speed. Impressively, the LSTM method yields remarkable precision levels of 97.433% for rainfall and an even higher accuracy of 99.018% for maximum wind speed forecasting. In essence, this study highlights LSTM's efficacy in disaster prediction with substantial accuracy.
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Hosseini, Seyed Mohsen, Alireza Aslani, Marja Naaranoja, and Hamed Hafeznia. "Analysis of Energy System in Sweden Based on Time series Forecasting and Regression Analysis." International Journal of Energy Optimization and Engineering 6, no. 3 (July 2017): 97–113. http://dx.doi.org/10.4018/ijeoe.2017070105.
Анотація:
Sweden has had a long-term political commitment to renewable energy development up until the oil crisis of the early 1970s. Oil accounted for more than 75 percent of Swedish energy supplies in 1970. Today, the figure is around 20 percent. In this study, Swedish energy system and the trend of energy consumption are analyzed to forecast total energy consumption and energy consumption in the sectors, industrial and residential, for the next ten years, therefore, most effective factors influencing energy consumption are identified in each sector. The present paper gives the additive Holt-Winter method and regression analysis, and the model selection is based on the square root of the average squared error. The results show that energy use in Swedish energy system, especially in the residential sector, will decrease between 2014 and 2024.
40
Sulandari, Winita, Yudho Yudhanto, Sri Subanti, Crisma Devika Setiawan, Riskhia Hapsari, and Paulo Canas Rodrigues. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data." Energies 16, no. 22 (November 8, 2023): 7495. http://dx.doi.org/10.3390/en16227495.
Анотація:
The importance of forecasting in the energy sector as part of electrical power equipment maintenance encourages researchers to obtain accurate electrical forecasting models. This study investigates simple to complex automatic methods and proposes two weighted ensemble approaches. The automated methods are the autoregressive integrated moving average; the exponential smoothing error–trend–seasonal method; the double seasonal Holt–Winter method; the trigonometric Box–Cox transformation, autoregressive, error, trend, and seasonal model; Prophet and neural networks. All accommodate trend and seasonal patterns commonly found in monthly, daily, hourly, or half-hourly electricity data. In comparison, the proposed ensemble approaches combine linearly (EnL) or nonlinearly (EnNL) the forecasting values obtained from all the single automatic methods by considering each model component’s weight. In this work, four electrical time series with different characteristics are examined, to demonstrate the effectiveness and applicability of the proposed ensemble approach—the model performances are compared based on root mean square error (RMSE) and absolute percentage errors (MAPEs). The experimental results show that compared to the existing average weighted ensemble approach, the proposed nonlinear weighted ensemble approach successfully reduces the RMSE and MAPE of the testing data by between 28% and 82%.
41
Persadanta, Pintanugra. "Airport Passenger Traffic Forecast: An Exploratory Study." Journal of Airport Engineering Technology (JAET) 1, no. 2 (March 30, 2021): 34–41. http://dx.doi.org/10.52989/jaet.v1i2.15.
Анотація:
Sultan Hasanuddin International Airport has been an important hub airport in Indonesia for decades, connecting traffic between west and east Indonesia as well as functioned as international gate in East Indonesia along with Sam Ratulangi Airport. Analysing characteristics of historical traffic data pattern, determining factors affecting past behaviour and building the best-fit model to forecast future traffic are critical for airport operator. Several forecast techniques are employed including Holt-Winter, Decomposition Method and Econometric Model. Moreover, trend, seasonal event and irregular phenomena from past data are observed to analyse traffic behaviour. Passenger traffic data from 1995 up to 2015 is utilized to predict future traffic until 2020. Validation of selected forecast model is conducted by implementing backtesting method which shows that the model successful foretell annual passenger movement with estimation average deviation around 0.5%. Some potential risks and opportunities as well as potential route expansion are identified to fortify future challenges.
42
Nissa, Dita Aulia, Sudradjat Supian, and Julita Nahar. "Inventory Control for MSME Products Using the Q Model with Lost Sales Condition Based on Products Sales Forecasting." International Journal of Quantitative Research and Modeling 4, no. 1 (March 4, 2023): 20–29. http://dx.doi.org/10.46336/ijqrm.v4i1.417.
Анотація:
Micro, Small and Medium Enterprises (MSMEs) have an important role in economic development in order to achieve thq quality of economic growth. Intense competition among MSMEs requires MSMEs to have a good inventory control that can help them minimize costs and maximize profits. One of the MSMEs that often experiences problems in inventory control is Sabun Bening Official. To solve the inventory problems in Sabun Bening Official, Holt-Winter Exponential Additive forecasting method is used as a guide to predict future product demand because product demand graph is seasonal and has trend pattern. After getting the value of product demand forecast, inventory control calucaltions are carried out using the Q Model probabilistic inventory method with lost sales condition. The uncertain and fluctuating demand causing the inventory system in Sabun Bening Official is probabilistic and the company will lose sales if it does not able to fulfill customer demands. Based on the research results, product forecasting for the coming period and inventory control policies which include the optimal number of product order, safety stock, reorder point, and product inventory costs can be obtained.
43
Sulandari, Winita, Subanar Subanar, Suhartono Suhartono, and Herni Utami. "Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model." International Journal of Advances in Intelligent Informatics 2, no. 3 (November 30, 2016): 131. http://dx.doi.org/10.26555/ijain.v2i3.69.
Анотація:
Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast
44
Huang, Yuwan. "Combination Prediction of Income Gap between Urban and Rural Residents in China Based on IOWA Operator." Asian Journal of Probability and Statistics 22, no. 4 (May 26, 2023): 31–40. http://dx.doi.org/10.9734/ajpas/2023/v22i4490.
Анотація:
Aims: Based on the income data of urban and rural residents in China from 1998 to 2021, the income gap variables of urban and rural residents were constructed, and the combination prediction method was used to predict the income gap between urban and rural residents in China.
Methodology: Grey prediction model GM (1,1), Holt-winter seasonless exponential smoothing model and autoregressive moving average ARIMA model were used to construct an order weighted arithmetic mean combination model induced by IOWA with the minimum sum of error squares. Then, by building new weights, three individual forecasting models and combination forecasting models are used to forecast the income gap between urban and rural residents in the next five years.
Results: The results show that the accuracy of the combined prediction model is significantly better than that of the single prediction model.
Conclusion: It can be known that the income gap between urban and rural residents will widen further in the future, with an average growth rate of 4.55%.
45
Barría-Sandoval, Claudia, Guillermo Ferreira, Katherine Benz-Parra, and Pablo López-Flores. "Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study." PLOS ONE 16, no. 4 (April 29, 2021): e0245414. http://dx.doi.org/10.1371/journal.pone.0245414.
Анотація:
Background Chile has become one of the countries most affected by COVID-19, a pandemic that has generated a large number of cases worldwide. If not detected and treated in time, COVID-19 can cause multi-organ failure and even death. Therefore, it is necessary to understand the behavior of the spread of COVID-19 as well as the projection of infections and deaths. This information is very relevant so that public health organizations can distribute financial resources efficiently and take appropriate containment measures. In this research, we compare different time series methodologies to predict the number of confirmed cases of and deaths from COVID-19 in Chile. Methods The methodology used in this research consisted of modeling cases of both confirmed diagnoses and deaths from COVID-19 in Chile using Autoregressive Integrated Moving Average (ARIMA henceforth) models, Exponential Smoothing techniques, and Poisson models for time-dependent count data. Additionally, we evaluated the accuracy of the predictions using a training set and a test set. Results The dataset used in this research indicated that the most appropriate model is the ARIMA time series model for predicting the number of confirmed COVID-19 cases, whereas for predicting the number of deaths from COVID-19 in Chile, the most suitable approach is the damped trend method. Conclusion The ARIMA models are an alternative to modeling the behavior of the spread of COVID-19; however, depending on the characteristics of the dataset, other methodologies can better predict the behavior of these records, for example, the Holt-Winter method implemented with time-dependent count data.
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Alay, F. Didem, Nagehan İlhan, and M. Tahir Güllüoğlu. "A Comparative Study of Data Mining Methods for Solar Radiation and Temperature Forecasting Models." JUCS - Journal of Universal Computer Science 30, no. 6 (June 28, 2024): 847–77. http://dx.doi.org/10.3897/jucs.109080.
Анотація:
Photovoltaic (PV) energy systems are a leading type of renewable energy systems globally. Predicting PV energy production accurately is crucial for maintaining efficient energy grids, making informed decisions in the energy market, and reducing maintenance costs. To ensure high accuracy and optimal production, it is essential to monitor and analyze these variables regularly. Solar radiation and temperature are two meteorological variables that directly affect the quantity of PV energy generated in PV facilities. The Performance Ratio (PR) is a critical parameter for assessing PV plant performance. A comprehensive model was constructed in this study to forecast solar radiation and temperature using multiple machine learning methods, including Instance-Based K-Nearest Neighbor Algorithm (IBK), Linear Regression, Random Forests, Random Tree, Multilayer Perceptron (MLP), and MLP Regression. Moreover, we used time series approaches, such as Simple Exponential Smoothing (SES), Error-Trend-Seasonality (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt Winter's Seasonal Method (HWES) models for PV systems prediction. Initially, we conducted daily forecasts as well as 1-step ahead forecasts at 5-minute intervals for both solar radiation and temperature. It is crucial to subject both variables to the same methodology in order to construct precise models for forecasting PV. Secondly, we compared the predicted values of solar radiation and temperature with the actual energy yield of the power plant to calculate energy production. Subsequently, a relative analysis of data mining models and time series models have been performed depending on the statistical error criteria like RMSE, MAPE, MABE, MAE, MSE, and direction accuracy (DAC).
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Yuan, Haibin, and Shengchen Liao. "A Time Series-Based Approach to Elastic Kubernetes Scaling." Electronics 13, no. 2 (January 8, 2024): 285. http://dx.doi.org/10.3390/electronics13020285.
Анотація:
With the increasing popularity of cloud-native architectures and containerized applications, Kubernetes has become a critical platform for managing these applications. However, Kubernetes still faces challenges when it comes to resource management. Specifically, the platform cannot achieve timely scaling of the resources of applications when their workloads fluctuate, leading to insufficient resource allocation and potential service disruptions. To address this challenge, this study proposes a predictive auto-scaling Kubernetes Operator based on time series forecasting algorithms, aiming to dynamically adjust the number of running instances in the cluster to optimize resource management. In this study, the Holt–Winter forecasting method and the Gated Recurrent Unit (GRU) neural network, two robust time series forecasting algorithms, are employed and dynamically managed. To evaluate the effectiveness, we collected workload metrics from a deployed RESTful HTTP application, implemented predictive auto-scaling, and assessed the differences in service quality before and after the implementation. The experimental results demonstrate that the predictive auto-scaling component can accurately predict the future trend of the metrics and intelligently scale resources based on the prediction results, with a Mean Squared Error (MSE) of 0.00166. Compared to the deployment using a single algorithm, the cold start time is reduced by 1 h and 41 min, and the fluctuation in service quality is reduced by 83.3%. This process effectively enhances the quality of service and offers a novel solution for resource management in Kubernetes clusters.
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Ye, Guo-hua, Mirxat Alim, Peng Guan, De-sheng Huang, Bao-sen Zhou, and Wei Wu. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach." PLOS ONE 16, no. 3 (March 16, 2021): e0248597. http://dx.doi.org/10.1371/journal.pone.0248597.
Анотація:
Objective Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. Method We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). Result There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). Conclusion The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.
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Putra, Toni Wijanarko Adi, Solikhin Solikhin, and M. Zakki Abdillah. "Model Hybrid untuk Prediksi Jumlah Penduduk yang Hidup dalam Kemiskinan." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 6 (December 30, 2023): 1253–64. http://dx.doi.org/10.25126/jtiik.1067484.
Анотація:
Kemiskinan merupakan permasalahan global yang saling berkaitan dengan permasalahan sosial lainnya. Sebagian besar negara berkembang di dunia pasti mengalami hal tersebut dan berusaha mencari solusi untuk mengentaskan kemiskinan, seperti yang terjadi di provinsi Jawa Tengah, Indonesia. Kemiskinan di Jawa Tengah mengalami fluktuasi selama lima tahun terakhir. Secara spesifik, menurut data Badan Pusat Statistik, jumlah penduduk miskin pada tahun 2018, 2019, 2020, 2021, dan 2022 sebanyak 3.897,20 ribu, 3.743,23 ribu, 3.980,90 ribu, 4.109,75 ribu, dan 3.831,44 ribu jiwa. Tinjauan terhadap naik turunnya kemiskinan pada tahun-tahun mendatang sangatlah penting. Untuk memerangi kemiskinan secara efektif, tidak hanya memahami penyebab kemiskinan tetapi memprediksi kemiskinan juga sangatlah penting. Penelitian ini bertujuan untuk memprediksi garis kemiskinan, jumlah penduduk miskin, dan persentase penduduk miskin di Jawa Tengah. Penelitian ini mengusulkan model peramalan hybrid untuk memperkirakan perubahan kemiskinan di Jawa Tengah. Di sini kami mengintegrasikan teknik statistik Holt-Winter triple exponential smoothing ke dalam fuzzy time series dengan pendekatan algoritma rate of change. Hasil uji kesalahan prediksi dengan metode Mean Absolute Percentage Error sangat kecil yaitu: garis kemiskinan sebesar 0,003%, jumlah penduduk miskin sebesar 0,005%, dan persentase penduduk miskin sebesar 0,004%. Temuan penelitian ini diyakini akan membantu pembuat kebijakan dalam mengembangkan strategi efektif untuk memerangi kemiskinan. Pengetahuan ini dapat menjadi dasar pengambilan keputusan alokasi sumber daya bagi pemerintah daerah dan pusat serta pembuat kebijakan. Abstract Poverty is a global problem that is interconnected with other social problems. Most developing countries in the world certainly experience this and are trying to find solutions to alleviate poverty, as is the case in the province of Central Java, Indonesia. Poverty in Central Java has fluctuated over the last five years. Specifically, according to data from the Central Statistics Agency, the number of poor people in 2018, 2019, 2020, 2021, and 2022 is 3,897.20 thousand, 3,743.23 thousand, 3,980.90 thousand, 4,109.75 thousand, and 3,831.44 thousand people. A review of the rise and fall of poverty in the coming years is very important. To fight poverty effectively, not only understanding the causes of poverty but also predicting poverty is essential. The aim of this research is to predict the poverty line, number of poor people, and percentage of poor people in Central Java. This research proposes a hybrid forecasting model to estimate changes in poverty in Central Java. Here we integrate Holt-Winter's triple exponential smoothing statistical technique into fuzzy time series with a rate of change algorithm approach. The prediction error test results using the Mean Absolute Percentage Error method are very small, namely: the poverty line is 0.003%, the number of poor people is 0.005%, and the percentage of poor people is 0.004%. It is believed that the findings of this research will assist policymakers in developing effective strategies to combat poverty. This knowledge can be the basis for resource allocation decisions for local and central governments and policymakers.
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Garus-Pakowska, Anna, Agnieszka Kolmaga, Ewelina Gaszyńska, and Magdalena Ulrichs. "The Scale of Intoxications with New Psychoactive Substances over the Period 2014–2020—Characteristics of the Trends and Impacts of the COVID-19 Pandemic on the Example of Łódź Province, Poland." International Journal of Environmental Research and Public Health 19, no. 8 (April 7, 2022): 4427. http://dx.doi.org/10.3390/ijerph19084427.
Анотація:
Legal highs are new psychoactive substances (NPSs) which pose a high risk for human health, and the spread of the SARS-CoV-2 pandemic has changed peoples’ behaviours, including the demand for NPS. The aim of the study was to assess both the frequency of intoxication with NPS in Łódź province over the period 2014–2020, and the impact of the COVID-19 pandemic on developing this trend. An analysis was carried out of data on intoxications in Łódź province in the years 2014–2020 reported by hospitals. The medical interventions rate (MI) per 100,000 people in the population was calculated. The frequency of intoxications was compared taking sociodemographic variables into account, and the effect of seasonal influence on intoxications was calculated using the Holt–Winter multiplicative seasonal method. In the period considered, there were 7175 acute NPS poisonings in the Łódź province and 25,495 in Poland. The averaged MI rate between 2014–2020 was 9.45 for Poland and 38.53 for the Łódź province, and the lowest value was found during the COVID pandemic in the year 2020 (respectively, 2.1 vs. 16.94). NPS users were mainly young men of 19–24 years old from a big city. Most cases were registered at weekends and in summer months. The majority of intoxications were caused by unidentified psychoactive substances of legal highs (chi2 = 513.98, p < 0.05). The actual number of NPS-related poisonings in the Łódź province in 2020 was lower than the value extrapolated from trend analysis of data between 2014–2019. NPS use in Poland decreased during the pandemic. It should be noted that a decrease in the number of drug-related incidents can have more than one reason, e.g., preventive programs, increased awareness, or changes in the law. This paper advocates that, in addition to monitoring NPS-related intoxications, there is further investigation into the social, cultural, and behavioural determinants of NPS to facilitate targeted prevention programmes and the development of new medical treatments.