Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital

Rachmat, Rachmat; Suhartono, Suhartono

doi:10.25008/bcsee.v1i2.8

Cited by 13 publications

(10 citation statements)

References 22 publications

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“…Single Exponential Smoothing (SES) could be a trusted forecast model as it is also the most used forecasting skill [14]. Summarized by Rachmat and Suhartono in 2020, SES is a method that gives an exponential weighted moving average for previous observations and shows no impact by the trend and the season [15]. It is a weighted average method that weights recent results more heavily.…”

Section: Time-series Forecasting Modelsmentioning

confidence: 99%

Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models

Tian,

Tian

2023

AEMPS

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Forecasting the stock market index has been an essential part of the investing process for the worlds investors, so predication for the composite stock market index of three different countries in North America were made for the investors to get references. The weekly data of three representative composite stock market index for each country from the past three months were chosen to generate the prediction for the next weeks performance of the stock market in each country through different time-series forecasting methods. The correlation between each index are calculated, indicating the short-term relationship between each countrys stock market. Three time-series predicting method is produced: SMA, WMA, and SES, one of the three methods with the least error by comparing the MSE and MAD would be selected for each stock index. Analyze the forecast results from the selected method for each countrys composite stock market index and compare them. The forecast results show that the composite stock market index for all three countries is going to decline in the following week. Several short-term relationships between different countries stock markets are revealed. The results and the discussion of this research tend to serve as a reference or an indicator for investors who have interests in multiple countries stock markets in the world.

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Section: Time-series Forecasting Modelsmentioning

confidence: 99%

Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models

Tian,

Tian

2023

AEMPS

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“…A primary goal of this field (TS-forecasting) is to furnish a reliable forecast by mining the inherent structure and hidden information of the TS and applying it to the model appropriately. Recent works suggest that in TS-forecasting, there exist many approaches, e.g., exponential smoothing (ES) [2,3], auto-regressive integrated moving average (ARIMA) [4,5] artificial neural network (ANN) [6,7], extreme learning machine (ELM) [8,9], facebook-prophet (FB-Prophet) [10,11], support vector regression (SVR) [12,13]. Researchers have also applied ensemble approaches for TS-forecasting and realized effective forecasting, as apparent from the pieces of literature [14−16].…”

Section: *Author For Correspondencementioning

confidence: 99%

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

2022

IJATEE

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Electricity and electric-equipment play a critical role in modern living, and precise price forecasting for these items aids decision-makers in anticipating changes, planning, and budgeting ahead of time. This present research focused on the wholesale price indexes of items from the "manufacture of electrical equipment (MEEQ)" group of India's existing wholesale price index (WPI) series. This work proposed a novel, state-of-art ensemble forecast approach that used multilayer perceptron (MLP) and Holt's linear (HL) approaches for some specified WPIs from this group. The researchers selected the WPIs that manifest non-linearity as determined by the curve-fit technique. The paper applied a variance-based weighted average scheme to generate the ensemble forecast. The statistical rigor-based curve-fit aided in identifying that seventeen out of forty-eight indexes manifest non-linear fits. The proposed MLP-HL ensemble approach exhibited excellent forecast results (relative root mean squared error, i.e., RRMSE < 10%) for all seventeen WPIs. For each of these seventeen WPI's, the current work compared the proposed MLP-HL with nineteen models (eight statistical, four machine learning, and seven contemporary ensemble strategies). In the majority of the cases, it outran the nineteen models in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE). As an alternative pathway for forecasting these seventeen non-linear WPIs, the present work suggests using this proposed MLP-HL approach.

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“…This approach employs a prediction equation as well as 2 smoothing equations (one for each level as well as one for each trend). [4,[7][8][9][10] ˆ…”

Section: Holt's Linear Exponential Smoothingmentioning

confidence: 99%

A Comparison between Brown’s and Holt’s Double Exponential Smoothing for Forecasting Applied Generation Electrical Energies in Kurdistan Region

Omer

Blbas

Kadir

2021

Cihan U Erbil SCI J

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The process of producing electricity from sources of energy is known as electricity production. Electric also isn't freely accessible in environment, thus it should be "manufactured" (i.e., converting another kinds of energy to electrical energy) by utilities with in electricity industry (transportation, distributing, and so on).Moreover, the objective of this study is to compared of Brown’s as well as Holt’s Double Exponential Smoothing also build a best forecasting time series model among two smoothing model forecasting, as well as focuses on optimizing characteristics to use the golden section technique. This exponential smoothing approach has been one of the time series forecasting methods that would be used to forecast (Generation Electrical) with in Kurdistan area. The issue that arises with this technique is determining the appropriate parameters to reduce predict inaccuracy. In addition, Data used in this paper are (Generation Electrical) in Kurdistan region for (132) months from 2010 to 2020. The study revealed that such data is trending modeled, indicating that a double exponential smoothing (DES) approach from Brown & Holt can be used with the (Stratigraphic & Minitab) software. There are the same results but the Result of analysis more depend on the R-program. The difference among the forecast findings acquired with optimum parameters as well as the assaying data was utilized to assess the feasibility of the forecast by completing normality and randomness tests. Ultimately, the outcomes of parameterization show that the optimal value of α that in DES Brown is (0.22) as well as the optimal MAPE is 9.23616 percent, whereas in DES Holt the optimal is (0.95) as well as the optimal β is (0.05) via the optimal MAPE of 8.08586 percent. This MAPE of a DES Brown technique is greater than the MAPE of a DES Holt approach. Feasibility experiments revealed that both approaches are capable of predicting. Depending on the value of MAPE as well as evaluation process, DES Holt's was recognized as the main prediction model.

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Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital

Cited by 13 publications

References 22 publications

Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models

Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

A Comparison between Brown’s and Holt’s Double Exponential Smoothing for Forecasting Applied Generation Electrical Energies in Kurdistan Region

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