scholarly journals PENGEMBANGAN ESTIMASI PARAMETER PADA METODE EXPONENTIAL SMOOTHING HOLT-WINTERS ADDITIVE MENGGUNAKAN METODE OPTIMASI GOLDEN SECTION (Studi Kasus: Wisatawan Mancanegara yang Menggunakan Jasa Akomodasi di DIY)

2018 ◽  
Vol 7 (4) ◽  
pp. 348-360
Author(s):  
Muhammad Aqajahs Al Qarani ◽  
Rukun Santoso ◽  
Diah Safitri
Keyword(s):  
Time Series Data ◽  
Forecast Error ◽  
Golden Section ◽  
Optimization Method ◽  
Exponential Smoothing ◽  
Series Data ◽  
Seasonal Patterns ◽  
Optimum Parameters ◽  
Golden Section Method ◽  
Selection Of

Forecasting is an activity to estimate what will happen in the future, one method that can be used is Exponential Smoothing. In this study used the smoothing method of Exponential Smoothing Holt-Winters Additive with three parameters that can be used for prediction of time series data that has trend patterns and seasonal patterns. The problem that arises in this method is to determine the optimum parameter to minimize the forecast error value. This study uses the Golden Section optimization method to estimate the optimum parameters that minimize the MAPE value. The data used is data on foreign tourists who use accommodation services in Yogyakarta from the period January 2009 to December 2016 that have trend patterns and additive seasonal patterns. In simplifying the optimization calculation process, a syntax using RStudio is arranged which contains the Golden Section algorithm to determine the combination that has the optimum parameters. In this optimization there are two treshold error, namely 0.001 and 0.00001. The results showed that the parameter estimator with the Golden Section method for the treshold error of 0.001 obtained MAPE of 18,96732% and for treshold error of 0.00001 MAPE was 18,96536%. This value is in the same MAPE criteria which is 10% ─ 20% (good) so that the selection of the best model is determined based on minimal iteration. Therefore the weighting parameter value used is the result of optimization with ε ≤ 0.001, then from the selected model it is used to predict the number of foreign tourists using accommodation services in Yogyakarta in the next 12 months.

scholarly journals PEMILIHAN PARAMETER OPTIMUM MENGGUNAKAN EXPONENTIAL SMOOTHING DENGAN METODE GOLDEN SECTION UNTUK PERAMALAN JUMLAH TITIK PANAS DI KALIMANTAN TIMUR

2021 ◽  
Vol 2 (2) ◽  
pp. 75-85
Author(s):  
NURA WALIDA ◽  
SRI WAHYUNINGSIH ◽  
FDT AMIJAYA
Keyword(s):  
Time Series Data ◽  
Golden Section ◽  
Exponential Smoothing ◽  
Smoothing Parameter ◽  
Smoothing Method ◽  
Series Data ◽  
East Kalimantan ◽  
Golden Section Method ◽  
Exponential Smoothing Method ◽  
Parameter Values

The exponential smoothing method is one method that can be used to predict time series data by smoothing the data. In this study, the method used was exponential smoothing with one smoothing parameter from Brown. The data used is the number of hotspots in East Kalimantan from January 2019 to September 2019. The purpose of this study is to obtain the optimum smoothing parameter values  for exponential smoothing from the results of the optimization process using the golden section method to minimize the MAPE value, to obtain forecasting results for each method in exponential smoothing for the number of hotspots in East Kalimantan from October to December 2019, and obtain a good exponential smoothing method to predict data on the number of hotspots in East Kalimantan. From this analysis, the researchers chose the methods used were DES and TES. The optimum smoothing parameter obtained at DES was 0,558430 and TES was 0,376352. The results of forecasting the number of hotspots obtained in DES in October were 2.142, November was 2.707, and December was 3.271 with a MAPE value of 95%. The TES method forecasting results were obtained in October as many as 2.193, November as much as 2.975, and December as many as 3.852  with a MAPE value of 108%. Based on the comparison of the MAPE values in the two methods, the DES method is better than the TES for calculating the predicted value of the number of hotspots in East Kalimantan, although the two methods are not yet suitable for handling this case. 

scholarly journals GOLDEN EXPONENTIAL SMOOTHING: A SELF-ADJUSTED METHOD FOR IDENTIFYING OPTIMUM ALPHA

2020 ◽  
Vol 5 (2) ◽  
pp. 587
Author(s):  
Fong Yeng Foo ◽  
Azrina Suhaimi ◽  
Soo Kum Yoke
Keyword(s):  
Golden Section ◽  
Exponential Smoothing ◽  
Series Data ◽  
Training Process ◽  
Times Series ◽  
Golden Section Search ◽  
Training Stage ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Accuracy Of Prediction

The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) was proposed to solve the problem. The conventional method was reformed and interposed with golden section search such that an optimum alpha which minimizes the errors of forecasting could be identified in the algorithm training process.  Numerical simulations of four sets of times series data were employed to test the efficiency of GES model. The findings show that the GES model was self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which was identified from the algorithm training stage, demonstrated good performance in the stage of Model Testing and Usage.

scholarly journals MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽  
2021 ◽  
Vol 36 (4) ◽  
pp. 354-363
Author(s):  
Anna Borucka ◽  
Dariusz Mazurkiewicz ◽  
Eliza Łagowska
Keyword(s):  
Time Series ◽  
Development Strategy ◽  
Time Series Data ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Rail Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Series Data ◽  
Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

Global optimization for parameter estimation of differential-algebraic systems

2009 ◽  
Vol 63 (3) ◽  
Author(s):  
Michal Čižniar ◽  
Marián Podmajerský ◽  
Tomáš Hirmajer ◽  
Miroslav Fikar ◽  
Abderrazak Latifi
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Time Series Data ◽  
Optimization Method ◽  
Differential Algebraic Equations ◽  
Series Data ◽  
Global Optimality ◽  
Algebraic Equations ◽  
Estimation Of Parameters ◽  
Orthogonal Collocation

AbstractThe estimation of parameters in semi-empirical models is essential in numerous areas of engineering and applied science. In many cases, these models are described by a set of ordinary-differential equations or by a set of differential-algebraic equations. Due to the presence of non-convexities of functions participating in these equations, current gradient-based optimization methods can guarantee only locally optimal solutions. This deficiency can have a marked impact on the operation of chemical processes from the economical, environmental and safety points of view and it thus motivates the development of global optimization algorithms. This paper presents a global optimization method which guarantees ɛ-convergence to the global solution. The approach consists in the transformation of the dynamic optimization problem into a nonlinear programming problem (NLP) using the method of orthogonal collocation on finite elements. Rigorous convex underestimators of the nonconvex NLP problem are employed within the spatial branch-and-bound method and solved to global optimality. The proposed method was applied to two example problems dealing with parameter estimation from time series data.

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals Storm-surge prediction at the Tanshui estuary: development model for maximum storm surges

2013 ◽  
Vol 1 (6) ◽  
pp. 7333-7356 ◽  
Author(s):  
C.-P. Tsai ◽  
C.-Y. You ◽  
C.-Y. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Storm Surges ◽  
Development Model ◽  
Series Data ◽  
Multilayer Perception ◽  
Optimum Parameters ◽  
Network Methods ◽  
Upstream Station

Abstract. This study applies artificial networks, including both the supervised multilayer perception neural network and the radial basis function neural network to the prediction of storm-surges at the Tanshui estuary in Taiwan. The optimum parameters for the prediction of the maximum storm-surges based on 22 previous sets of data are discussed. Two different neural network methods are adopted to build models for the prediction of storm surges and the importance of each factor is also discussed. The factors relevant to the maximum storm surges, including the pressure difference, maximum wind speed and wind direction at the Tanshui Estuary and the flow rate at the upstream station, are all investigated. These good results can further be applied to build a neural network model for prediction of storm surges with time series data.

scholarly journals Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

2011 ◽  
Vol 2 (2) ◽  
pp. 636 ◽  
Author(s):  
Iwa Sungkawa ◽  
Ries Tri Megasari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Data Series ◽  
Past Data ◽  
The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape. 

scholarly journals Perancangan Aplikasi Peramalan untuk Metode Exponential Smoothing Menggunakan Aplikasi Lazarus (Studi Kasus: Data Konsumsi Listrik Kota Samarinda)

2021 ◽  
pp. 57-70
Author(s):  
Hairi Septiyanor ◽  
Syaripuddin Syaripuddin ◽  
Rito Goejantoro
Keyword(s):  
Golden Section ◽  
Electricity Consumption ◽  
Exponential Smoothing ◽  
Consumption Data ◽  
Forecasting Method ◽  
Double Exponential ◽  
Application Procedure ◽  
Golden Section Method ◽  
Smoothing Parameters ◽  
Exponential Smoothing Method

Exponential smoothing is forecasting method used to predict the future. Lazarus is an open source software based on free pascal compiler. at this research, program Lazarus be design used exponential smoothing method to predict electricity consumption data in Samarinda City from September to November 2018. Purposed of this researched is to determine the procedure of building an exponential smoothing forecasting application and obtained forecasting result using the built application. Procedure of built the application are designed interface, designed properties and filled coding. The optimum smoothing parameters were obtained used the golden section method. Based on the analysis, electricity consumption data in Samarinda City shows a trend pattern, then the forecasting was used double exponential smoohting (DES) method are DES Brown and DES Holt. The best forecasting method for at this researched is DES Holt, because DES Holt method produced MAPE 0,0659% less than DES Brown method produced MAPE 0,0843%.

scholarly journals Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing

2021 ◽  
Vol 3 (4) ◽  
pp. 45-53
Author(s):  
Tresna Maulana Fahrudin ◽  
Prismahardi Aji Riyantoko ◽  
Kartika Maulida Hindrayani ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Time Series Data ◽  
Polynomial Regression ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Training Set ◽  
Double Exponential ◽  
Forecasting Performance ◽  
One Step ◽  
Double Exponential Smoothing

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.

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