Winters Exponential Smoothing Method Research Articles

Comparison Of Single Moving Average And Winter Exponential Smoothing Methods In Predicting The Number Of Divorce Cases At The Religious Court Of Cibinong

Based on data from the Central Bureau of Statistics, the divorce rate in Indonesia shows a tendency to increase from year to year. Similar conditions were experienced by Cibinong district. The forecasting method that will be used in this study is the Single Moving Average and Winter's Exponential Smoothing. The results of forecasting the number of lawsuits in 2023 from July to December with movements with two obtained forecasts in July were 602, August 283, MAD value = 67.29, MSE value = 8.722, MAPE = 11.61, RMSE = 1.46, Accuracy value of 88.39%. Movements with four forecasting periods in July were 620, August 448, September 301, October 141. MAD value = 99.33, MSE value = 14,722, MAPE = 16.59, RMSE = 1.90, Accuracy value of 83.41%. Forecasting with the Winters Exponential Smoothing Method with Alpha: 0.1, Beta: 0.3 and Gamma: 0.5, the forecast results obtained in July were 504.10026, August 491.61306, September 663.18788, October 745.41004, November 732.42766 and December 732.10904. MAE value is 171.65116, MSE value is 686.63361, MAD value is 38.74, MSE value is 2.797 MAPE value is 6.0 and RMSE value is 0.83 and accuracy value is 94.00%. Based on the calculation results above, it is concluded that forecasting the number of divorce filings at Cibinong Religious Court from January 2018 to December 2023 with the Winter Exponential Smoothing method has MAD, MSE, MAPE, RMSE values smaller than values in the Single Moving Average method. Winter Exponential Smoothing method is more appropriate with an accuracy value of 94.00%.

On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field.

FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD

Airplanes provide comfort and speed for their users, especially for those who have limited time. The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. The data used is secondary data on the number of airplane passengers at Ahmad Yani International Airport from 2012 to 2022 obtained from PT Angkasa Pura 1 (Persero). The Holt Winters Exponential Smoothing method is used because it aligns with the data pattern that includes trends and seasonality in the research, and it has a low level of accuracy. In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. This study aims to predict the number of airplane passengers at Ahmad Yani International Airport in Semarang using the Holt Winters Exponential Smoothing and ELM methods. The results of the analysis show that the MAPE value in the Holt Winters Exponential Smoothing method is 8,18% and in the ELM method using 12 input neurons and 43 neurons in the hidden layer, a MAPE of 6,04% is obtained. so that the ELM method is the right method for predicting the number of airplane passengers at Ahmad Yani International Airport in Semarang.

WINTERS EXPONENTIAL SMOOTHING UNTUK FORECASTING HARGA SAHAM PT ASTRA INTERNATIONAL TBK

Investing in the stock market is one of the investment options that are in great demand by investors. Investors generally invest because want to gain profits which can be seen from the stock returns. On the other hand, there is a risk of loss if the investor makes the wrong decision. Investors need accurate information, predictions and a sense of security in stock transactions. Investors or shareholders need to know the development of stock prices in the future with forecasts to anticipate large losses. This study aims to predict the share price of PT. Astra International Tbk (ASII.JK) one year ahead using the winters exponential smoothing method. Forecasting results show that ASII's share price for the next year will increase every month. This can provide information on future stock movements for investors as a consideration for investing in these stocks.

Perbandingan Fuzzy Time Series Lee untuk Meramalkan Nilai Tukar Petani di Provinsi Gorontalo

Gorontalo Province is one of the provinces in Indonesia where 60% of the population are farmers and fishermen. As much as 28,66% of PDRB in Gorontalo Province in 2020 was contributed by the agricultural sector. Farmer's Exchange Rate is a measurement capability of agricultural products in producing goods or services. Therefore, NTP forecasting is needed so that it becomes a reference in the future in making a decision to increase the agricultural sector. In this study, a comparison was made of the Holt Winters Exponential Smoothing method with Lee's Fuzzy Time Series to find out which is the best forecasting method for predicting NTP in Gorontalo Province. Based on the forecasting results, the accuracy value obtained from FTS Lee has a mape value of 0,65557% for FTS Lee order 1 and 0,55607%. While the accuracy value obtained by the multiplicative Holt Winters Exponential Smoothing is 5.92509% and the additive Holt Winters Exponential Smoothing is 6,14574%. From the forecasting results obtained, it can be concluded that the best method for predicting NTP in Gorontalo Province is the FTS Lee Order 2 method.

PERAMALAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA MENGGUNAKAN METODE HOLT WINTERS EXPONENTIAL SMOOTHING DAN FUZZY TIME SERIES MARKOV CHAIN

One of the public transportation choices by the public is the train. The number of train passengers on the island of Java often increases and decreases in certain months. PT.KAI can monitor the number of train passengers by forecasting. Forecasting aims to predict the number of train passengers so that PT.KAI is ready to provide the best service. This study uses monthly data on the number of train passengers on Java Island from January 2015 to February 2020. This study uses multiplicative holt winters exponential smoothing and fuzzy time series markov chain. The multiplicative Holt Winters exponential smoothing method is used on data that contains trend and seasonal elements that experience data fluctuations simultaneously. The fuzzy time series markov chain method is a combination of the fuzzy time series with the markov chain which aims to obtain the greatest probability using the transition probability matrix. Based on the analysis results, it can be concluded that the multiplicative holt winters exponential smoothing method is better at predicting the number of train passengers on Java Island because the value of sMAPE multiplicative holt winters exponential smoothing is smaller, it is 3,0643% and the sMAPE fuzzy time series markov chain value is 5,2955%.

Peramalan Banyaknya Penumpang Bandar Udara Internasional Sam Ratulangi Manado Dengan Metode Winter's Exponential Smoothing dan Seasonal ARIMA

The Winter's Exponential Smoothing method is used to overcome seasonal patterns in data. This method is divided into two models, namely additive and multiplicative models. While the Seasonal ARIMA method is an ARIMA method used to solve seasonal time series. The data used is secondary data from PT. Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado for the period January 2015 to December 2022. The purpose of this research is to determine the model for forecasting the number of passengers at PT.Angkasa Pura I (Persero) Sam Ratulangi International Airport Manado, as well as to compare the Winter's method Exponential Smoothing and Seasonal ARIMA based on the smallest MSD value. The results of the comparison of the two methods with the smallest MSD value are the Winter's Exponential Smoothing method with the multiplicative model equation. The results of the analysis on arriving passengers, namely the exponential smoothing of the original data (α) is 0.9, the smoothing of the trend pattern (β) is 0.1, and the smoothing of the seasonal pattern (γ) is 0.1. With the results of the 2023 forecast, namely: January 95,046, February 87,154, March 98,462, April 97,391, May 110,061, June 103,098, July 130,360, August 118,165, September 108,790, October 115,673, November 112,114, and December 136.40. The results of the analysis on domestic passenger departures are α = 0.9, β = 0.1, and γ = 0.2. With the results of forecasting the number of departures in 2023, namely January 108.900, February 88.588, March 100.646, April 98.066, May 111.638, June 112.963, July 126.684, August 111.471, September 111.872, October 116.211, November 111.990, and December 117.431.

PERBANDINGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN EXTREME LEARNING MACHINE UNTUK PERAMALAN JUMLAH BARANG YANG DIMUAT PADA PENERBANGAN DOMESTIK DI BANDARA UTAMA SOEKARNO HATTA

The loading of goods carried out at the airport is an essential part of the transporting goods system. In this regard, it is necessary to have a prediction to make the right policy or to solve the problems that occur. Holt Winter's Exponential Smoothing, which one of the classic methods of analyzing time series data, and Extreme Learning Machine which is part of the artificial neural network method, are methods that can be used as a tool for forecasting problems. Holt Winter's Exponential Smoothing uses three times of smoothing on related data, which are level smoothing, trend smoothing, and season smoothing, while Extreme Learning Machine goes through three stages, which are normalization, training, and denormalization. In measuring the error rate in related forecasting, the symmetric Mean Absolute Percentage Error (sMAPE) value is used. The Holt Winter's Exponential Smoothing method Additive model produces a sMAPE value of 26.14%; while the Multiplicative model with the same method resulted in the sMAPE value of 25.69%. For the Extreme Learning Machine method, the sMAPE value is 49.85%. Based on the accuracy test using the sMAPE value, Holt Winter's Exponential Smoothing method Multiplicative model is the better method than Extreme Learning Machine

Comparison of Exponential Smoothing Methods for Forecasting Marine Fish Production in Pekalongan Waters, Central Java

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.

PERBANDINGAN METODE SEASONAL ARIMA DAN METODE EXPONENTIAL SMOOTHING DALAM MERAMALKAN KUNJUNGAN WISATAWAN MANCANEGARA KE BALI

Tourism is one of the main sectors in increasing a country's economy. For this reason, an estimate of tourist visits to Bali is needed so that it can be used as a benchmark for development and development in the tourism sector. Forecasting the number of foreign tourists coming to Bali using the Seasonal ARIMA method produces an MSE value of 1,250,276,526. While the Winter's Exponential Smoothing method obtained 1,291,150,258. The results of the forecast for the number of foreign tourists coming to Bali in 2020 using the SARIMA method are 588,980, 653,740, 659,817 (January-March 2020).

The tourist demand of the hotel chain. time series for a forecast model

In an increasingly uncertain world where world dynamics accelerates the way of managing processes in any sector, the forecast of tourist demand becomes very important. In this sense, the present research aims to forecast the tourist demand of the Cuban Hotel Chain of Pinar del Río, Cuba, until December 2019, through the use of time series techniques, which facilitate planning and decision-making. in this sector and in this way work towards the achievement of an integration in the productive chains, considering that tourism is one of the socioeconomic activities that activates many other sectors of production and services, as well as predicting the behavior of tourism. For this, the quantitative research method was used as the guiding method, based on the Box - Jenkins methodology and the Holt - Winters exponential smoothing method. It is also possible to characterize tourism management taking into consideration two indicators: cost per weight and average income per tourist, referring to efficiency and effectiveness respectively. In addition, a multivariate analysis of time series was carried out that made it possible to characterize the tourist activity in four fundamental stages in the hotel chain taken as the object of study.

PERAMALAN KEDATANGAN WISATAWAN MANCANEGARA INDONESIA: METODE HOLT’S WINTER EXPONENTIAL SMOOTHING

This reseacrh aims to predict the arrival of foreign tourists in Indonesia using the Exponential Smoothing method. This research is quantitative descriptive. The data used are data of foreign tourist arrivals according to nationality taken from the Badan Pusat Statistik. Data is managed through the Microsoft Excel application. In determining RMSE, Solver Parameter help is used in Microsoft Excel to determine the lowest error rate. The data used in this research indicate that there are trend and seasonal patterns, so the most suitable Exponential Smoothing method is the Holt's Winter Exponential Smoothing method. The results of this research indicate that foreign tourist arrivals in Indonesia are predicted to increase in 2020. The results of this research are expected to help the government and related agencies in planning and decision making in the tourism industry.

METODE EXPONENTIAL SMOOTHING EVENT BASED (ESEB) DAN METODE WINTER’S EXPONENTIAL SMOOTHING (WES) UNTUK PERAMALAN JUMLAH PENUMPANG TIBA DI PELABUHAN PENYEBERANGAN GORONTALO

Forecasting the number of passengers can be a consideration for managers of Gorontalo Crossing Port regarding the provision of tickets. The number of passengers can be influenced by certain seasonal or special events. To see the special events that affect the number of passengers arriving at Gorontalo Crossing Port, the forecasting method used is the Exponential Smoothing Event Based (ESEB) method. The seasonal influences can be known through historical data patterns and using the Winter’s Exponential Smoothing (WES) method. After compared, the ESEB method is a better method of forecasting the number of arriving passengers at Gorontalo Crossing Port because it has a smaller MAPE value than the WES method.

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.

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology

Abstract This paper describes daily peak load forecasting using an adaptive hybrid two-stage methodology. Because the time series of electricity consumption is mainly influenced by seasonal effects, the double seasonal Holt–Winters exponential smoothing method is firstly used for next-day peak electricity demand forecasting. In the second stage, the secondary forecasting model is applied taking into account the benefits of Fuzzy c-means clustering; K-nearest neighbors algorithm; Wavelet packet decomposition; and Adaptive Neuro-Fuzzy Inference System, for further improvement in forecasting accuracy. The whole architecture of the proposed model will be presented and the results will be compared with neural networks and stand-alone adaptive neuro-fuzzy inference system based approaches by using a gathered data from the Algerian power system. The results show that: (1) the proposed methodology is the best among all the considered schemes, (2) the FKW-ANFIS has satisfactory performance in both normal and special daily conditions.

An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China

In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO–NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt–Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt–Winters exponential smoothing method.

Analysis and forecasting of nonresidential electricity consumption in Romania

Electricity consumption forecast has fundamental importance in the energy planning of a country. In this paper, we present an analysis and two forecast models for nonresidential electricity consumption in Romania. A first part of the paper is dedicated to the estimation of GDP and price elasticities of consumption. Nonresidential short run GDP and price elasticities are found to be approximately 0.136 and −0.0752, respectively, whereas long run GDP and price elasticities are equal to 0.496 and −0.274 respectively. The second part of the study is dedicated to the forecasting of nonresidential electricity consumption up to year 2020. A Holt–Winters exponential smoothing method and a trigonometric grey model with rolling mechanism (TGMRM) are employed for the consumption prediction. The two models lead to similar results, with an average deviation less than 5%. This deviation is to be considered acceptable in relation to the time horizon considered in the present study.

Forecasting the primary energy demand in Turkey and analysis of cyclic patterns

The planning and estimation of future energy demand via modern statistical methods have been officially used in Turkey since 1984. However, almost all previous forecasts proved significantly higher than actual observations because of several reasons discussed here. The cycle analysis, which is a semi-statistical technique that makes use of any cyclicity in the historical data of annual additional amounts of energy demand, appears to give better results than the other techniques for forecasting energy demand in Turkey. This method suggests that the energy demand will be around 130 million toe in 2010. This figure is very close to the estimates obtained by the Winter's exponential smoothing method. To increase the scientific validity of the method, it should be applied in other similar countries.

Forecasting New Zealand Tourism Demand with Disaggregated Data

This paper compares the forecasting performance of the ARIMA model and the Winters Exponential Smoothing method against each other and the naive No Change process. The models are fitted to quarterly international tourist flow data to New Zealand, from June 1978 to September 1992. Forecasting performance is compared between 11 different countries and world regions with the travel flow divided by type of tourism into categories including Holiday travel, VFR travel and Business travel. It is concluded that the Winters and ARIMA methods outperform the No Change process. In all cases, the relative performance between ARIMA and Winters is affected by whether tourism is disaggregated by types of tourist travel, or analysed only as total flow.