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Human Immunodeficiency Virus (HIV) merupakan virus yang dapat memicu kerusakan pada sistem kekebalan tubuh manusia, menyebabkan infeksi pada orang yang terkena serta dapat mengurangi sistem kekebalan tubuh dan jika tidak segera disembuhkan akan terjangkit penyakit lain yang disebut dengan Acquired Immuno Deficiency Syndrom (AIDS). Kebutuhan obat ARV bagi ODHA mempengaruhi kebutuhan stok obat yang harus dipasok pemerintah ke Kabupaten/Kota. Dinas Kesehatan Provinsi bertanggung jawab atas perencanaan kebutuhan obat dari pemerintah Kabupaten/Kota, serta penerimaan permintaan obat, penyimpanan, pendistribusian, pencatatan dan pelaporan mutasi obat. Tujuan dari penelitian ini ialah untuk memahami bagaimana rangkaian proses pada supplier, dan penyimpanan obat agar obat tidak mengalami kekosongan atau kelebihan stok dengan menganalisis Rantai Pasok dan melakukan peramalan pada metode Double Exponential Smoothing yang didapatkan hasil presentase error terkecil pada obat Tenofovir dengan nilai MAPE 14,7%. Kata kunci: Stok obat, HIV ARV, Rantai pasok, Double Exponential Smoothing

Food is a source of basic needs for every living creature, so food security is an interesting issue for every country. This raises problems regarding food and land use, especially in Sungai Penuh City. Food problems arise due to a lack of information regarding appropriate land use and the productivity of the land itself. In the current industrial era 4.0, forecasting can be done using information technology tools that provide convenience and efficiency in forecasting times and can be integrated with geographic information systems. The forecasts made by the community are based on past experience without considering the factors that influence crop yields, so that they can cause losses both in terms of time and costs. Apart from that, less accurate predictions of food yields can lead to less than optimal development of food security which has an impact on meeting food needs. This research involved respondents from the Department of Agriculture and Food Security, namely agricultural and food experts. The method for collecting data in this research is observation and interviews. This research analyzes harvest data for the 2018-2023 period sourced from the Central Statistics Agency using the Double Exponential Smoothing method by considering error values with α = 0.1 and 0.5 and β = 0.1 and 0.5. The calculation of the smallest error value is: ME = 80.92, MAD = 5.58, MAPE = 11%, MSE = 52.69 by combining the value of α= 0.1 and the value of β = 0.1 to produce a prediction of the corn harvest in Kumun Debai District in 2024 of 45 tons and year 2025 as much as 40 tons.

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Ginger, a vital herbal commodity, experiences low yield rates, necessitating intensive cultivation and rigorous evaluation by farmers to ensure financial viability and alignment with market demands. This study was conducted to devise a harvest forecasting system that supports decision-making through minimal error rates by comparing double exponential smoothing (DES) and long short-term memory (LSTM) forecasting methods. The efficacy of these methods was assessed through a series of trials, analyzing data collected from 2015 to 2019, comprising 250 datasets. The evaluation focused on two primary metrics: the Mean Absolute Percentage Error (MAPE) and the Root MSE (RMSE), to determine the precision of forecast models. It was observed that the LSTM model outperformed the DES method, yielding a MAPE of 38.99% and an RMSE of 1244.85432, in contrast to the DES method which resulted in a MAPE of 43.49% and an RMSE of 12997.34261, at an alpha level of 0.4 and an optimal beta of 0.1. Given these findings, the LSTM model is recommended for the forecast of ginger yields due to its superior accuracy and lower standard error compared to the DES method. This comparative analysis underscores the importance of selecting appropriate forecasting models to enhance agricultural planning and productivity, particularly in crops with fluctuating yields such as ginger. [ABSTRACT FROM AUTHOR]

This research paper introduces an innovative approach to predicting failures in Local Area Network (LAN) switches, combining Double Exponential Smoothing (DES) with a suite of Machine Learning (ML) algorithms including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM). The primary objective of this study is to enhance the accuracy and timeliness of LAN switch failure predictions, thereby facilitating more proactive and effective network management. Our methodology involves the integration of DES for trend analysis and forecasting in time-series data, with the advanced predictive capabilities of the aforementioned ML algorithms. This hybrid approach not only leverages the strengths of DES in identifying underlying patterns in failure data but also capitalizes on the diverse predictive models to handle various aspects of failure prediction more robustly. The paper details the process of data collection, preprocessing, and the specific application of DES and each ML algorithm to the dataset. A notable contribution of this research is the development of a framework that effectively combines the output of DES with ML models, leading to a significant improvement in predictive accuracy as compared to traditional methods. Through rigorous testing and validation; the proposed approach demonstrated a marked increase in the precision and reliability of failure predictions. The results indicate that the integration of DES with ML algorithms can substantially aid in preemptive maintenance and decision-making processes in LAN management. The implications of these findings are profound, suggesting that such a combined approach can greatly enhance network stability and efficiency. While the focus of this study is on LAN switches, the methodology has the potential for broader applications in various fields of network management and predictive maintenance.

Obtaining reliable and high-accuracy prediction results of displacement trends in the long term is crucial for mitigating geohazards. Deep learning can capture dynamic and nonlinear characteristics in long-term time series and is widely used in landslide displacement prediction. However, its black-box attribute prevents decision-makers from understanding the basis of the model output, which limits the application of the model in the final optimization scenario. Thus, a novel "decomposition-prediction-addition-interpretative" framework including variational mode decomposition (VMD), double exponential smoothing (DES), and a long short-term memory network with spatiotemporal attention mechanism (TSAM-LSTM) is proposed. It enables high-precision multi-step prediction and spatiotemporal dimension interpretability analysis. Therein, VMD decomposes the total displacement into the trend, periodic, and random displacement, and the random displacement is further decomposed by VMD. On this basis, DES is used to predict the trend displacement, TSAM-LSTM to predict the periodic and random displacements, and finally, all the predicted values are superimposed to realize the total displacement prediction. The performance of the proposed approach was validated using monitoring data from H8 accumulation, Huangzangsi water conservancy project. The results indicate that the VMD-DES-TSAM-LSTM can achieve satisfactory prediction results. The introduction of TSAM improves the generalization ability of the traditional LSTM and significantly enhances its prediction performance. Meanwhile, TSAM accurately reveals the most relevant temporal and spatial information contained in input data that affects target displacement and visualizes the attention focus during model training, which provides a more favorable basis for model optimization and disaster prevention decision-making. [ABSTRACT FROM AUTHOR]

The CPI is useful for knowing the increase in income, prices, can also be used as an economic indicator and a benchmark for the amount of production costs. The Consumer Price Index is often used to measure the level of increase or decrease (inflation/deflation) in the prices of goods and services that are the basic needs of the people of an area, as well as a consideration for salary adjustments, retirement wages and others. CPI forecasting in Sorong City is a solution to predict future economic conditions as seen from the increase or decrease in the CPI and choose the right countermeasure or strategy in dealing with the consequences of an increase (inflation) or decrease (deflation) in the CPI that will occur in the future. The Covid-19 Pandemic situation has greatly affected economic and social life, including the CPI in Sorong City. The purpose of this research is to obtain the right model in forecasting the CPI in Sorong City using the Double Exponential Smoothing Brown method, a forecasting method that uses one weighting parameter. The best model of forecasting before the Covid-19 pandemic using optimal weighting parameters with a MAPE value of 0.647%, namely: and the best model during the Covid-19 pandemic using optimal weighting parameters with a MAPE value of 0.633%. The forecasting model obtained, namely: Ft+m = 107,304 + 0,522(m) . [ABSTRACT FROM AUTHOR]

In Indonesia, the plantation sub-sector has an important role in increasing state revenue through the exports of its products, besides the mining and gas sector. The most widely produced plantation crops in Indonesia are oil palm and rubber and South Kalimantan is one of the top 10 provinces in Indonesia with oil palm plantations. This study aims to detect the correct forecasting model for data on oil palm crops and rubber production in South Kalimantan Province and to analyse the forecasting results for oil palm crops and rubber in South Kalimantan Province using the double exponential smoothing method. This research was conducted for 8 months (March 2022 to December 2022), using observational data from 2001 to 2021. Double Exponential Smoothing Holt was used in this study by looking at the error value obtained with the smallest Mean Absolute Percentage Error (MAPE). For palm oil production, the parameters α=0.8 and β=0.6 were the best parameters with a MAPE value of 8.05% and resulted in the forecasting of oil palm crops production in 2022 not increasing, in 2023 and 2024 experiencing an increase of 1%. As for forecasting rubber production, the parameters α=0.9 and β=0.9 are the best parameters with a MAPE value of 5.45% and forecasting rubber production in 2022 will increase by 1%, in 2023 and 2024 by 2%.

The Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES) are forecasting methods that require two and three smoothing parameters, respectively. Smoothing parameters are often determined through a trial and error process that is not really efficient since many experiments need to be done. Therefore, in this study, a smoothing parameter estimation algorithm is conducted in the form of the modified Golden Section Search (GSS) to obtain the optimal smoothing parameters from the DES and TES methods. Forecasting is carried out on production, domestic consumption, and export consumption data of Indonesian coffee, which is one of the leading agricultural sub-sector commodities. The data is obtained from the Ministry of Agriculture of the Republic of Indonesia. The smoothing parameters obtained by applying the modified GSS are used to forecast production and domestic consumption data using the DES method, while the forecasting of the export consumption data is done with the TES method. All of the MAPE values are less than 20% which indicates that the smoothing parameters obtained by using the modified GSS are able to perform good forecasting. The results show that coffee production in Indonesia cannot meet its demand until 2024 since the total coffee consumption exceeds the production.

Copyright of Eastern Mediterranean Health Journal is the property of World Health Organization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Poverty is the inability to meet the minimum standard of living. This situation can be seen from the low income in meeting basic needs, both in the form of needs for food, clothing, and shelter. Based on Law No. 24 of 2004, poverty is a socioeconomic condition of a person or group of people for which the non-fulfillment of its basic rights to maintain and develop a dignified life. The issuance of this policy from the government is due to differences in social, economic, and natural characteristics and conditions in each region, with the aim of maximizing the potential in each region, so that it is expected to be able to improve welfare and reduce poverty. Yogyakarta Special Region (D.I.Y) is a province-level Special Region in Indonesia located in the southern part of Java Island, and bordering Central Java Province. According to the BPS report, the number of diy poor people until September 2021 was 474.49 thousand people. This study used Double Exponential Smoothing as the analysis method. The results of the analysis obtained are data on the poverty line of the Yogyakarta Special Region from 2004 to 2022 patterned on upward trand. the MAPE value of the model is 1.8% which indicates that the forecasting model uses the DES method accurately. 4. After looking at the comparison plot of the actual data and forecasting. From 2023 to 2027, the poverty line in the Special Region of Yogyakarta will continue to increase compared to the previous year

Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings inaccuracy due to the intrinsic merits and demerits of the models. In this work, oil monitoring and fault pre-warning of wind turbine gearboxes were studied based on oil inspection data of three wind turbines that have been working continuously for 3.5 years. The Grey Model (GM) and the Double Exponential Smoothing (DES) were combined by a modified inverse-variance weighting method proposed in this work, which used relative errors to calculate weight coefficients, reducing the errors and improving the accuracy as a whole. The predicted data were compared with the measured data to verify the predicting accuracy. Subsequently, a statistical method and linear regression method were adopted to jointly develop a pre-warning threshold for the oil inspection data. Comparing the predicted data with the threshold, the results showed that one of the wind turbines was in a warning state. The prediction was validated by an endoscope inspection of the gearbox, which found that some parts were slightly worn. [ABSTRACT FROM AUTHOR]

Forecasting is a technique that uses past data or historical data to determine something in the future. Forecasting methods with time series models consist of several methods, such as Double Exponential Smoothing (Holt method) and ARIMA. DES (Holt method) is a method that is used to predict time series data that has a trend pattern. ARIMA model combines AR and MA models with differencing order d. The poverty line is calculated by finding the total cost of all the essential resources that an average human adult consumes in one year. The lack of poverty reduction in an area is the lack of information about poverty. The selection of the forecasting method was made by considering several things. The Exponential Smoothing method was chosen because this method was able to predict time series financial data well and revise prediction errors. While the ARIMA method is better for short-term prediction, it can predict values that are difficult to explain by economic theory and are efficient in predicting time series financial data. There is still little research on comparing time series data for forecasting methods. Researchers are interested in comparing the Exponential Smoothing and ARIMA methods in implementing poverty line forecasting in Central Java. The two methods are compared by determining the best method for forecasting the poverty line in Central Java. The best forecasting method can be seen from the MAPE value of each method

In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, and omni-directional landslide displacement prediction is more scientific than single point prediction or interval prediction. In this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), and Gaussian process regression (GPR) is proposed for the point, interval, and probabilistic prediction of landslide displacement. The proposed model includes two parts: (i) predicting the inherent evolution trend of landslide displacement through DES-VMD-LSTM; (ii) evaluating the uncertainty in the first prediction based on the GPR model. In the first part, DES is used to predict the trend displacement, and the periodic and stochastic displacement in the residual displacement is extracted by VMD and predicted by the LSTM. The triggering factors of the periodic and stochastic displacement are screened by the maximum information coefficient (MIC), and the screened factors are decomposed into low- and high-frequency components by VMD to predict the periodic and stochastic displacements, respectively. The first cumulative displacement prediction results are achieved by adding the predicted trend and the periodic and stochastic displacements. By setting the first predicted displacement as the input and the actual displacement as the expected output, the point, interval, and probabilistic predictions of landslide displacement are achieved through the GPR model. The plausibility of the proposed model is validated with data from the Bazimen (BZM) and Baishuihe (BSH) landslides in the Three Gorges Reservoir area. This model has the potential to achieve the deterministic prediction of landslide displacement and quantify the uncertainty contained in the displacement. A comparative study shows that this method has a high performance for the point, interval, and probabilistic prediction of landslide displacement. [ABSTRACT FROM AUTHOR]

ABSTRAK Indonesia dan Prancis adalah dua Negara yang mengalami Covid 19 dengan pola pergerakan kasus Covid 19 yang berbeda. Kondisi Indonesia masih mengalami siklus one wave namun Prancis sudah masuk pada pola second wave. Makna second wave adalah kondisi epidemi Covid 19 yang baru muncul setelah epidemi sebelumnya dianggap selesai. Dalam peramalan kasus Covid 19 baik itu terkait informasi puncak dari terjadinya kasus Covid 19 serta ramalan terkait akan berakhirnya pandemi kasus Covid 19 suatu negara merupakan hal penting bagi pemerintah suatu Negara. Model hybrid meningkatkan akurasi ramalan dibandingkan model time series yang dilakukan secara terpisah. Tujuan penelitian ini adalah melakukan peramalan kasus Covid 19 di Indonesia dan Prancis dengan menggunakan metode hybrid dan membandingkan dengan peramalan dengan salah satu metode tunggal. Metode yang digunakan adalah metode tunggal yaitu Nonlinear Regression Logistic dan metode Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing. Hasilnya adalah model peramalan Hybrid Nonlinear Regression Logistic and Doubel Exponential Smoothing lebih bagus digunakan dalam peramalan kasus Covid 19 di Indonesia dan Prancis. Terlihat bahwa nilai MAPE model Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing jauh lebih kecil dibandingkan model peramalan Nonlinear Regression Logistic. ABSTRACT Indonesia and France are two countries that have experienced Covid 19 with different patterns of movement of Covid 19 cases. Indonesia's condition is still experiencing a one wave cycle but France has entered into the second wave pattern. The meaning of the second wave is the condition of the Covid 19 epidemic which only emerged after the previous epidemic was considered over. In forecasting the Covid 19 case, whether it is related to the peak information on the occurrence of the Covid 19 case and predictions regarding the end of the pandemic of the Covid 19 case in a country, it is important for the government of a country. The hybrid model improves forecast accuracy compared to the time series model which is carried out separately. The purpose of this study is to forecast the cases of Covid 19 in Indonesia and France using the hybrid method and comparing with forecasting with one single method. The method used is a single method, namely Nonlinear Logistic Regression and Hybrid Nonlinear Regression Logistic-Double Exponential Smoothing methods. The result is that the Hybrid Nonlinear Regression Logistic and Double Exponential Smoothing forecasting model is better used in forecasting the Covid 19 cases in Indonesia and France. It can be seen that the MAPE value of the Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing model is much smaller than the Nonlinear Regression Logistic forecasting model.

The objective of this study was to design an agent model for forecasting demand for rubber-based automotive parts using the Agent Based Modelling and Double Exponential Smoothing (ABMDES) approach. Model design in rubber-based automotive spare parts forecasting using the integration of Agent Based Modelling (ABM) approach and Double Exponential Smoothing (DES) technique was done using agent design approach based on class diagram and definition function for each agent in mathematics models including DES-based forecasting. The ABM design has a structure consisting of an agent ID, an attribute to be calculated by the computer and function / process consisting of a function that has values ​​and voids (unstructured). Combining ABM and DES can guide us to see the forecast accuracy, monitoring the estimation of stock shortage and the excess of stock due to errors in DES forecast. Therefore, the Agent Based Modelling-Double Exponential Smoothing (ABMDES) approach is suitable for modelling the demand of rubber-based automotive parts in business simulation.

Peramalan adalah kegiatan memperkirakan kejadian yang akan terjadi berdasarkan historical data kuantitatif suatu kejadian. Peramalan sering digunakan oleh pemerintah dalam membuat suatu kebijakan. Salah satu kebijakan pemerintah adalah menurunkan angka kemiskinan setiap tahunnya. Penelitian ini bertujuan untuk membangun sistem Peramalan Tingkat Kemiskinan Kabupaten Pangkep berbasis desktop untuk memberikan gambaran jumlah tingkat kemiskinan periode selanjutnya. Dalam penelitian ini, metode peramalan yang digunakan adalah Double Exponential Smoothing dengan nilai alpha 0.001, 0.01, 0.2, 0.3, 0.5, 0.7, 0.8, 0.99, dan 0.999. Dengan menggunakan data angka kemiskinan dari tahun 2010 sampai 2019, diperoleh bahwa dari 9 nilai alpha yang digunakan, tingkat kesalahan terkecil yaitu 1.2% diberikan oleh alpha 0.5 yang diukur menggunakan metode Mean Absolute Percentage Error (MAPE). Adapun tingkat akurasi peramalan yang didapatkan jumlah kesalahan tiap alpha sebesar 95.394%.

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precision feeding and management of growing-finishing pigs typically require mathematical models to forecast individual pig performance from past data. The current approaches, namely double exponential smoothing (DES) and dynamic linear regression are likely to have some limitations in their applicability since they: (1) assume that responses can be forecasted linearly, which only holds in the short-term, and (2) often take insufficient account of uncertainty and correlations in the estimated traits. We developed and evaluated alternative approaches to forecasting individual growth or intake responses based on nonlinear models (allometric, monomolecular, rational) and Bayesian methodology to fit models to the data and generate probabilistic forecasts. We applied these approaches to individual data from two distinct pig populations, to parameterise the models (fitting based on a training dataset) and forecast performance (forecast horizons: 1–30 d tested on a validation dataset). We found that good fitting did not guarantee accurate forecasting, which is quantitatively relevant in the medium-to-long term. Forecasts from nonlinear models were more accurate compared to those from benchmark linear models, with the allometric model being more accurate for most pigs across considered forecast horizons. While DES was the best model at fitting, it was also the least accurate at forecasting for all forecast horizons. These results enhance the understanding of how underlying biological growth responses could be approximated using straightforward mathematical relationships. The approach could be utilised to formulate optimised feeding strategies and inform management decisions, including pen allocation or end-weight prediction. • Nonlinear and linear predictive models of individual pig growth or intake were tested. • Bayesian forecasting offered key advantages relative to previous estimation methods. • Good fit of a model to the early data does not guarantee accurate forecasts. • Nonlinear model forecasts (1–30 d ahead) were the most accurate (≈ 0.5–8.0%). • Allometric models can be useful tools for forecasting pig growth responses. [ABSTRACT FROM AUTHOR]

Accurate and reliable displacement prediction is vital for developing landslide early warning system, since displacement variations directly reveal the evolution of landslide. The past decade has seen the rapid development of displacement prediction technology. Most of the existing studies are based on deterministic point prediction models, with the goal of obtaining high-precision predictions. However, these pointwise predictions do not give any indication of credibility, which makes it difficult to make accurate decisions for subsequent landslide treatment. To address this problem, a prediction interval estimation method, instead of point prediction, for landslide displacement is proposed. In the methodology, double exponential smoothing is employed to deal with the nonlinear cumulative displacement, while two support vector machines are proposed to directly generate the displacement lower boundary and upper boundary. To obtain optimal model parameters, the adaptive chicken swarm optimization using chaotic mapping and adaptive inertia weight strategies is proposed to minimize a modified prediction interval-based objective function. A real-world landslide displacement data set is used to demonstrate the proposed method. Experimental results show that it is a promising tool for the construction of high-quality prediction intervals, which can inform the landslide treatment-related decision-making. [ABSTRACT FROM AUTHOR]

Purpose: As we know, COVID-19 affects the economy of most of the world, one of the investment media that continues to grow is mutual fund investment. This study aims to find the right time to invest by studying data in the previous period. Design/methodology/approach: In this study, we collect daily data on Net Asset Value (NAV) prices from January to September 2021, by analyzing calculations using the double exponential smoothing method to predict the time with the lowest price for investing. Findings: The results showed that this study was able to predict the right time to invest in mutual funds using the Holt Double Exponential Smoothing method using the actual smoothing data parameter of 0.9 (alpha) and the trend data smoothing parameter of 0.1 (beta) with produces a Mean Absolute Percentage Error (MAPE) value of 0.85343. The right time to invest in this research is determined on November 2, 2021 with an estimated net asset value (NAV) of 2348.644032. Paper type: Research paper Keywords: Mutual Fund, Investment Time, Investment Tool, Double Exponential Smoothing.

Beras adalah sumber karbohidrat utama yang digunakan warga indonesia sebagai bahan makanan pokok, sehingga ketersediaan dan harga turut menjadi perhatian. Tujuan dari penelitian ini adalah melakukan peramalan harga beras bulanan untuk tahun 2019, sekaligus membandingkan 2 metode peramalan yaitu Double Moving Average dan Double Exponential Smoothing untuk mendapatkan hasil peramalan harga beras terbaik. Data yang digunakan dalam penelitian ini adalah harga beras eceran dari bulan Januari 2011 hingga Maret 2019. Berdasarkan pengujian yang dilakukan didapatkan metode Double Moving Average lebih baik dengan nilai MAPE mencapai 0,582542%, dan nilai MSE mencapai 6349,25 menggunakan orde waktu 3. Rata-rata hasil peramalan harga beras eceran bulanan untuk tahun 2019 dengan menggunakan metode DMA sebesar Rp.12169,-

In this paper, the Double Exponential Smoothing (DES), the Multiplicative Holt-Winters (MHW) and the Additive Holt-Winters (AHW) methods with optimal initial values and weighting factors are presented to forecast lime, Thai chili, and lemongrass prices in Thailand. Since these plants are important economic plants in Thailand, knowing their market prices or trends before selling them would be very useful for Thai agriculturists to appropriately plan their work and sales. In this paper, lime, Thai chili and lemongrass prices from January 2011 to September 2016 were gathered from the website’s database of Simummuang market used and as input data. This is one of the biggest markets in Thailand. Our study reveals that the DES method with optimal initial values and weighting factors provides the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE) when forecasting Thai chili and lemongrass prices, while the MHW and the AHW methods show better performance on forecasting lime price data which present seasonality patterns. The forecasted prices of lime, Thai chili and lemongrass during October 2016 to December 2016 are also provided.

Road freight transport often requires the prediction of volume. Such knowledge is necessary to capture trends in the industry and support decision making by large and small trucking companies. The aim of the presented work is to demonstrate that application of some artificial intelligence methods can improve the accuracy of the forecasts. The first method employed was double exponential smoothing. The modification of this method has been proposed. Not only the parameters but also the initial values were set in order to minimize the mean absolute percentage error (MAPE) using the artificial immune system. This change resulted in a marked improvement in the effects of minimization, and suggests that the variability of the initial value of S2 has an impact on this result. Then, the forecasting Bayesian networks method was applied. The Bayesian network approach is able to take into account not only the historical data concerning the volume of freight, but also the data related to the overall state of the national economy. This significantly improves the quality of forecasting. The application of this approach can also help in predicting the trend changes caused by overall state of economy, which is rather impossible when analysing only the historical data.

Penelitian ini bertujuan untuk melakukan peramalan penjualan di masa mendatang beserta fitur-fitur aplikasi lainnya seperti kemampuan untuk menambahkan (insert), mengubah (edit), dan menghapus (delete) data produk dan ringkasan penjualan yang akan atau telah disimpan dalam suatu database untuk studi kasus perusahaan tertentu. Aplikasi yang dibangun menggunakan algoritma double exponential smoothing. Alasan pemanfaatan algoritma ini adalah nilai smoothing akan diperoleh tepat waktu sebelum data aktual saat data historis memiliki komponen trend. Oleh karena itu, untuk nilai smoothing tunggal, perlu ditambahkan nilai dari double smoothing untuk menyesuaikan trend tersebut. Metode yang dapat digunakan untuk memprediksi trend linear adalah metode dua parameter Holt. Pada metode Holt, nilainya bukan trend halus dari double smoothing secara langsung, melainkan penghalusan trend dilakukan dengan menggunakan parameter yang berbeda dengan yang digunakan untuk menghaluskan data aslinya. Hasil penelitian menunjukkan bahwa aplikasi yang dibangun memiliki persentase kesalahan sekitar 28,38%. Aplikasi yang dibangun juga mampu memenuhi fitur-fitur yang dikehendaki oleh user dalam pemrosesan produk dan rekapitulasi.

Abstract A new hybrid forecasting tool is developed in this study which makes use of satellite remote sensing data of surface solar irradiation coupled to a Double Exponential Smoothing time series model. The prediction capabilities of the Double Exponential Smoothing model are reported to be higher than the ARMA and NAR-Neural Network. The mean absolute percentage error of this hybrid system is revealed to be the lowest (4.89%) on average for 5 consecutive days-ahead forecasts over the years 2013–2015, with the smallest standard deviation reported throughout the year, characteristic of a highly stable and robust model (3.83 W / m 2 ). Exploring the performance of the model for the best and worst case scenarios reveal that high prediction accuracies on both spatial and temporal scales are achievable with strong positive linear correlations of the orders of 0.928 and 0.894 respectively, averaged over the 5 days forecasts. The performance of the hybrid system is found to be higher as compared with benchmark accuracy reached by several other models employed in literature. Finally, the use of the hybrid forecasting tool developed in providing energy and grid management facilities for the island of Mauritius is also presented. Highlights • A novel hybrid forecasting method is developed using satellite remote sensing and time series model. • High forecasting accuracies are achieved on spatial scales up to four days ahead. • Validation techniques, including a principal component analysis, are performed. • Application of the forecasting tool in the energy management situation of Mauritius is presented. [ABSTRACT FROM AUTHOR]

A novel hybrid model composed of least squares support vector machines (LSSVM) and double exponential smoothing (DES) was proposed and applied to calculate one-step ahead displacement of multifactor-induced landslides. The wavelet de-noising and Hodrick-Prescott filter methods were used to decompose the original displacement time series into three components: periodic term, trend term and random noise, which respectively represent periodic dynamic behaviour of landslides controlled by the seasonal triggers, the geological conditions and the random measuring noise. LSSVM and DES models were constructed and trained to forecast the periodic component and the trend component, respectively. Models’ inputs include the seasonal triggers (e.g. reservoir level and rainfall data) and displacement values which are measurable variables in a specific prior time. The performance of the hybrid model was evaluated quantitatively. Calculated displacement from the hybrid model is excellently consistent with actual monitored value. Results of this work indicate that the hybrid model is a powerful tool for predicting one-step ahead displacement of landslide triggered by multiple factors. [ABSTRACT FROM AUTHOR]

The remaining life prediction problem of gear is studied and a new set of gear life prediction program is put forward based on double exponential smoothing（ DES） and improved incremental support vector machine for regression（ SVR）. The new program utilizes principal component analysis（ PCA） to filtrate fusion index set,and then using DES to process fusion index set as the input of improved incremental SVR. Meanwhile,the improved incremental SVR prediction model is constructed based on incremental Latin hyper- cube sampling（ LHD） and Direct search algorithm. Compared with the standard SVR prediction model based on original characteristic values,the interference of signal randomness and mutation of the new model sre overcome,furthermore,the latest evolution trend information of the gear status is obtained,the optimization effect and prediction performance is obviously improved. The validation analysis of the new model is carried out by using the full life- cycle test data,the results show that the improved incremental SVR prediction model obtained a more reliable,more stable predictions,it has certain practical value in engineering.

The performance of the Autoregressive Integrated Moving Average )ARIMA) model and Double and Holt-winters exponential smoothing techniques for forecasting the consumption of particleboard in Iran are compared. Annual time series data from 1978 to 2009 in the modeling process, and observations from 2010 to 2012 were used to check the accuracy of the models’ forecasting performance. Also, the models’ performances were calculated in terms of RMSE criterion, and the consumption of particleboard in Iran was forecasted up to the year 2017 using the most appropriate model. The results of comparing different forecast models showed that the ARMA (2,1) model yielded the lowest RMSE value compared to the other two models, which makes it more appropriate for the prediction of consumption of particleboard in Iran. Results also revealed that there might be an increasing trend in the consumption of particleboard, i.e., an average annual increasing rate calculated as 5% for particleboard. Thus, it was predicted that the consumption of particleboard would increase from 901,652 m3 in 2012 to 1,178,320 m3 in 2017.

ABSTRACT: The mean maximum monthly temperature data were recorded at 112 stations in Australia. The data (1990–2015) were downloaded from the Australian Bureau of Meteorology (BOM) website. Missing values were imputed using regression models based on information from the nearest stations, as well as the time periods. The data were deseasonalized to remove seasonal variations and then cluster analysis techniques were used to group the stations into six clusters. For each cluster, locally weighted scatter‐plot smoothing (LOESS) and double exponential smoothing (DES) were used to analyse temperature changes and patterns. The results showed that LOESS produced better fits as well as smoother curves compared with the DES. The trends in temperature were increasing in all clusters, whereas the patterns showed periodicity of the temperatures. [ABSTRACT FROM AUTHOR]

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In this paper, the Double Exponential Smoothing (DES), the Multiplicative Holt-Winters (MHW) and the Additive Holt-Winters (AHW) methods with optimal initial values and weighting factors are presented to forecast lime, Thai chili, and lemongrass prices in Thailand. Since these plants are important economic plants in Thailand, knowing their market prices or trends before selling them would be very useful for Thai agriculturists to appropriately plan their work and sales. In this paper, lime, Thai chili and lemongrass prices from January 2011 to September 2016 were gathered from the website's database of Simummuang market used and as input data. This is one of the biggest markets in Thailand. Our study reveals that the DES method with optimal initial values and weighting factors provides the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE) when forecasting Thai chili and lemongrass prices, while the MHW and the AHW methods show better performance on forecasting lime price data which present seasonality patterns. The forecasted prices of lime, Thai chili and lemongrass during October 2016 to December 2016 are also provided. [ABSTRACT FROM AUTHOR]