Application of Self-Adaptive Exponential Smoothing Method in the Water Quality Forecast of Poyang Lake

2012 ◽  
Vol 518-523 ◽  
pp. 1464-1467
Author(s):  
Bin Xiang Liu ◽  
Qun Cao ◽  
Xiang Cheng
Keyword(s):  
Water Quality ◽  
Dynamic Characteristic ◽  
Poyang Lake ◽  
Exponential Smoothing ◽  
Smoothing Parameter ◽  
Smoothing Method ◽  
Forecast Result ◽  
Exponential Smoothing Method ◽  
Water Quality Forecast ◽  
Self Adaptive

The smoothing parameter is a constant when forecasting water quality using exponential smoothing, which usually renders the error to be enlarged, but the assumption of constant is out of accord with the practice. Based on the deep analysis of deficiency of traditional exponential smoothing, this paper establishes self-adaptive exponential smoothing model and compares the forecast result. It is proved that the dynamic characteristic of water quality can be better reflected and the forecasting precision can be improved further by self-adaptive exponential smoothing model.

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 TRIPLE EXPONENTIAL SMOOTHING: FORECASTING PERBANDINGAN PENUMPANG KERETA API DAN PESAWAT TERBANG

2020 ◽  
Vol 18 (2) ◽  
pp. 277
Author(s):  
Khoirin Azaro ◽  
Nur Indah Riwajanti ◽  
Anik Kusmintarti
Keyword(s):  
Consumer Demand ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Seasonal Patterns ◽  
Efficient Planning ◽  
Exponential Method ◽  
Exponential Smoothing Method ◽  
Airline Company

This research aims to predict the number of train and airplane passengers in 2020. Forecasting of train and airplane passengers is interest to analyze and estimate consumer demand to help the train or airline company prepare effective and efficient planning. This type of research is descriptive quantitative and uses data taken from the Indonesian Statistic Agency (BPS). Data were analyzed using Exponential smoothing Method. Train and airplane passenger data shows trend and seasonal patterns so that the exponential method used is Triple Exponential smoothing. The results of the study show that train passengers in 2020 are increase. While forecast results related to aircraft passengers in 2020 also tend to increase.

scholarly journals Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method

2019 ◽  
Vol 125 ◽  
pp. 23006
Author(s):  
Dyna Marisa Khairina ◽  
Aqib Muaddam ◽  
Septya Maharani ◽  
Heliza Rahmania
Keyword(s):  
Reference Value ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Tax Revenue ◽  
Regional Office ◽  
Forecasting Error ◽  
The Government ◽  
Exponential Smoothing Method ◽  
Single Exponential

Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.

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