scholarly journals Forecasting of socio-economic security indicators by means of exponential smoothing

2019 ◽  
Vol 5 (3) ◽  
pp. 241-249
Author(s):  
D. Shvaiba
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Forecast Model ◽  
Exponential Smoothing ◽  
Economic Security ◽  
Exponential Law ◽  
The Past ◽  
Predicted Values ◽  
Security Indicators ◽  
Weighted Moving Average

The method of exponential smoothing is widely used in the forecasting of financial and economic characteristics in different sectors of the economy, departments, etc. In the construction of a forecast model by exponential smoothing time series of characteristics of socio–economic security is smoothed with the support of a weighted moving average, in which the weights obey the exponential law. In this case, the following levels of the series are given significant values in comparison with the past, because they carry more important information to determine the predicted values at the level of socio-economic security characteristics.

scholarly journals USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

2021 ◽  
Vol 8 (2) ◽  
pp. 117-122
Author(s):  
Sambas Sundana ◽  
Destri Zahra Al Gufronny
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential ◽  
Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu  metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

scholarly journals CAPACITY AVAILABILITY ANALYSIS TO HP SERVER PROLIANT DL380P GEN8 AT PT. AERO SYSTEM INDONESIA

2020 ◽  
Vol 2 (1) ◽  
pp. 141-148
Author(s):  
Naufal Rizki Rinditayoga ◽  
Dewi Nusraningrum
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Forecasting ◽  
Flight Data ◽  
Availability Analysis ◽  
Capacity Limit ◽  
Single Exponential ◽  
Weighted Moving Average

There has Servers who used for Keeping some domestic flight data at Soekarno-Hatta airport and its often experience downtime or servers inconnected, because these server capacity exceeds those maximum server limit. This research aims to examine and analyze capacity from HP Proliant DL380P Gen8 server that used for domestic flight data at PT. Aero Systems Indonesia. The population here used 3 servers with research sample is 1 server, HP Proliant DL380P Gen8 server. Data analysis exert time series forecasting used comparison from Moving Average, Single Exponential Smoothing and Weighted Moving Average methods. These results which using Moving Average shows that the use of server capacity exceeds those server capacity limit with highest usage up to 3,568 GB from total available capacity of 2,930 GB, so it needs to change immediately by other server capacity which more balanced with usage at PT. Aero Systems Indonesia.

scholarly journals Forecasting End of COVID – 19 in India Based on Time Series Analysis

2020 ◽  
Vol 5 (9) ◽  
pp. 763-772
Author(s):  
B S Kambo ◽  
Dr.Kulwinder Kaur
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
P Value ◽  
The Past ◽  
Lack Of Fit ◽  
Q Statistic ◽  
Past Trend

In this paper, the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models for active and exponential smoothing HOLT for removed rates has been estimated using daily time series data from 1st April to 14thSeptember 2020.The active and removed rates are computed from cumulative confirmed, active, recovered and deceased cases. It has been found that ARIMA (0, 1, 1) and Holt exponential smoothing Models are best fit for active and removed rates respectively. Normalized BIC is 0.577and 0.898 for active and removed rates respectively and is minimum among all the six models considered. Lack of fit of models is tested by Ljung-BoX Q statistic. The pvalue is 0.925 and 0.840 for active and removed rates respectively Since for both the rates p-value is greater than 0.05, hence conclude that our model does not show a lack of fit. On the basis of our analysis, active rate will be nullified latest by 5th January 2020, if everything goes best, as P M of India has assured on eve of Independence Day that vaccine for corona will be available very soon. Otherwise by 9 th February 2021 if the past trend continued and in worst situation it will tends to zero on 26th March 2021. We expect the removed rates will reach 100 percent by 20TH October 2020 if everything goes best and by 5th January 2021 if the past trend continued. On the assumptions that Pandemic will come to an end when removed rate in the population tends to 100 percent and active rate to zero percent. Thus on the basis our analysis we expect that COVID – 19 Pandemic may come to end latest either by 9 th February 2021 or 26th March 2021 subject to condition that the social distance and safely measures remains vigilance to stabilize and control the pandemic and in achieving India’s recovery from COVID-19.

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Agus Sulaiman ◽  
Asep Juarna
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

scholarly journals Kajian Peramalan dan Pengaruh Profitabilitas terhadap Harga Saham pada Perusahaan Makanan dan Minuman Tahun 2011-2016

2017 ◽  
Vol 8 (1) ◽  
pp. 75-88
Author(s):  
Octaviani Hutahaean ◽  
Abdul Basith
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Profit Margin ◽  
Moving Averages ◽  
Return On Assets ◽  
Return On Equity ◽  
Purposive Sampling ◽  
Net Profit ◽  
Weighted Moving Average

Laju pertumbuhan industri terbesar selama tahun 2011-2015 yaitu 8,48 persen terhadap Produk Domestik Bruto (PDB) mencerminkan perusahaan yang termasuk dalam industri makanan dan minuman memiliki kinerja bisnis yang baik. Penelitian ini bertujuan untuk mengetahui kondisi harga saham dan profitabilitas pada tahun 2011-2015, mengetahui peramalan harga saham dan profitabilitas pada tahun 2016 dan untuk menganalisis pengaruh profitabilitas terhadap harga saham pada tahun 2011-2016. Analisis profitabilitas dipresentasikan oleh beberapa rasio keuangan yaitu Return On Equity (ROE), Return On Assets (ROA), Net Profit Margin (NPM), dan Earning Per Share (EPS). Penelitian ini menggunakan teknik purposive sampling dan data yang digunakan merupakan data sekunder. Peramalan menggunakan metode moving averages, weighted moving average, dan exponential smoothing dengan nilai MAD terkecil menggunakan aplikasi POM-QM for windows-3. Model analisis yang digunakan dalam penelitian ini adalah regresi linier berganda dengan menggunakan SPSS 18. Hasil penelitian menunjukkan bahwa PT Delta Djakarta, Tbk (DLTA) memiliki kondisi harga saham, ROE, ROA, dan EPS dengan rata-rata tertinggi selama 2011-2015. PT Tiga Pilar Sejahtera Food, Tbk (AISA) memiliki rata-rata NPM tertinggi selama 2011-2015. PT Delta Djakarta, Tbk (DLTA) dan PT Indofood Sukses Makmur, Tbk (INDF) menunjukkan peramalan tahun 2016 terhadap harga saham dan profitabilitas mengalami peningkatan dari tahun sebelumnya. Profitabilitas berpengaruh simultan dan signifikan terhadap harga saham dan secara parsial menunjukkan bahwa ROE dan EPS berpengaruh dan signifikan terhadap harga saham.

scholarly journals Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
R. S. Sparks ◽  
T. Keighley ◽  
D. Muscatello
Keyword(s):  
Public Health ◽  
Time Series ◽  
Steady State ◽  
Regression Model ◽  
Moving Average ◽  
Outbreak Detection ◽  
Exponential Weights ◽  
Health Records ◽  
High Incidence ◽  
Weighted Moving Average

Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average (EWMA) plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forecasts of the next day's count. Departures of counts from their forecasts are monitored. The paper outlines an approach for improving early outbreak data signals by dynamically adjusting the exponential weights to be efficient at signalling local persistent high side changes. We emphasise outbreak signals in steady-state situations; that is, changes that occur after the EWMA statistic had run through several in-control counts.

scholarly journals Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods

2006 ◽  
Vol 38 (3) ◽  
pp. 513-523 ◽  
Author(s):  
Dwight R. Sanders ◽  
Mark R. Manfredo
Keyword(s):  
Time Series ◽  
Soybean Oil ◽  
Soybean Meal ◽  
Vector Autoregression ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Forecast Evaluation ◽  
Time Series Modeling ◽  
Var Models ◽  
Smoothing Techniques

A battery of time series methods are compared for forecasting basis levels in the soybean futures complex: soybeans, soybean meal, and soybean oil. Specifically, nearby basis forecasts are generated with exponential smoothing techniques, autoregression moving average (ARMA), and vector autoregression (VAR) models. The forecasts are compared to those of the 5-year average, year ago, and no change methods. Using the 5-year average as the benchmark method, the forecast evaluation results suggest that alternative naive techniques may produce better forecasts, and the improvement gained by time series modeling is relatively small. In this sample, there is little evidence that the basis has become systematically more difficult to forecast in recent years.

scholarly journals Sales forecasting in a mechanical component manufacturer: comparison between monte carlo simulation and time series analysis

2019 ◽  
Vol 10 (4) ◽  
pp. 1324
Author(s):  
Kevin William Matos Paixão ◽  
Adriano Maniçoba da Silva
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Moving Average ◽  
Prediction Methods ◽  
Future Scenario ◽  
Sales Forecast ◽  
Forecasting Methods ◽  
Weighted Moving Average

Organizations today are required to be prepared for future situations. This preparation can generate a significant competitive advantage. In order to maximize benefits, several companies are investing more in techniques that simulate a future scenario and enable more precise and assertive decision making. Among these techniques are the sales forecasting methods. The comparison between the known techniques is an important factor to increase the assertiveness of the forecast. The objective of this study was to compare the sales forecast results of a mechanical components manufacturing company obtained through five different techniques, divided into two groups, the first one, which uses the fundamentals of the time series, and the second one is the Monte Carlo simulation. The following prediction methods were compared: moving average, weighted moving average, least squares, holt winter and Monte Carlo simulation. The results indicated that the methods that obtained the best performance were the moving average and the weighted moving average attaining 94% accuracy.

scholarly journals Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

2019 ◽  
Vol 7 (10) ◽  
pp. 894-909
Author(s):  
Rhuan Carlos Martins Ribeiro ◽  
Thaynara Araújo Quadros ◽  
John Jairo Saldarriaga Ausique ◽  
Otavio Andre Chase ◽  
Pedro Silvestre da Silva Campos ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Mean Square Error ◽  
High Performance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Mean Square

Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.

scholarly journals PREDIKSI JUMLAH PENERIMAAN SISWA SMK SWASTA TAHUN AJARAN 2011/2012

2012 ◽  
Vol 3 (2) ◽  
pp. 923
Author(s):  
Haryadi Sarjono
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Vocational High School ◽  
Absolute Deviation ◽  
Squared Error ◽  
Forecasting Methods ◽  
Naive Method ◽  
Manual Calculation ◽  
Weighted Moving Average

This study aims to determine prediction number of modern private Vocational High School (SMK) students in a province in Borneo with the approach of six forecasting methods: Linear Regression, Exponential Smoothing with Trend, Exponential Smoothing, Weighted Moving Average, Moving Average, and the Naive Method, besides using Manual calculation, the approach of QM for windows is used as a comparison. The result will be determined by the six forecasting methods which is used as a proper basis for the next calculating based on the smallest MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) approach. The data in this study were made by the writer alone. 

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