Linear Forecast Method Research Articles

Perencanaan luas produksi pada Perusahaan Tahu Putra Jaya di Kota Kupang

The purpose of this study was to determine the production area planning at the Putra Jaya Tofu Company in Kupang City. The research method used is using Trend Linear forecasting method and Break Even Point analysis. Based on the sales data for the last five years, it can be determined that the production area planning is obtained from the forecasting results and the Break Even Point analysis is carried out. The results showed that the company does not have a specific method in planning the area of ​​production. This has an impact on excess inventory or product residue which results in an increase in inventory costs. For the area of ​​tofu production at the Putra Jaya Tofu Company in Kupang City in 2020-2022, it has increased by 3% per year compared to the previous period. It is recommended for the Tahu Putra Jaya company to plan production area using the Trend Linear forecasting method and perform a Break Even Point analysis so that it is known that the production area planning experiences sales profits.

Pengendalian Stok Spareparts Mobil Dengan Metode EOQ dan Min-Max Inventory

This research was conducted in the distributor of car spare parts and the object studied was car glass glue. This item is a product that experiences a stacking stock in the warehouse, so it needs inventory control to reduce the stacking stock. The character of the product which does not have expired date as long as it is not used and small size, the company never make an optimal estimate of the order, so the activity of ordering goods is only based on its own estimates. The method used in this research is EOQ to obtain the economical amount of the product to be ordered. The calculation process using EOQ method is done against 2019 sales data predicted to 2021 using Trend Linear forecasting method, with function form is Y=1,8776x+256,21. Furthermore, the minimum and maximum stock quantity is determined using the Min-Max Inventory method. From the result of the study found that the economic Q per ordered is 386 pcs with the frequency of bookings 8 times a year. The minimum inventory is 108 pcs and the maximum is 144 pcs with the amount of safety stock is 72 pcs and ROP is 108 pcs. By using EOQ method, the company can make savings of Rp3,939,330 per year and reduce storage cost by Rp10,119,635 per year.

PENERAPAN METODA PERAMALAN UNTUK MENETAPKAN MPS DAN MRP PADA STRIPER BUSHING (STUDI KASUS DI RUANG PRODUKSI UNIT STRIPER BUSHING PADA CV. SURYA BAJA MANDIRI KARAWANG)

Company CV. Surya Baja Mandiri is a service company in the form of a privately owned workshop that produces spare parts products, especially automotive, most of whose products are ordered directly from abroad, namely Japan, for example for the production of Stripper Bushing. To fulfill quality orders and on time according to the order schedule (Due Date) in order to maintain the satisfaction of the company that is the producer and still get efficient results for the company, a production schedule for the supply of materials that is truly effective is needed.
 To determine a production schedule and supply of materials that are truly effective, the author begins by doing a forecast using the company's past demand data starting from May 2020 to April 2020 which is an average of 8000 pcs per month. The Holt forecasting method was chosen because it gives the smallest MSE value of 28113.96 compared to the Brown forecasting method 741098966 and the Trend Linear forecasting method 2381480.62

Agricultural Sector FDI and Economic Growth in Saarc Countries

The study seeks to establish the relationship between foreign direct investment to Saarc region agricultural sector and economic growth with secondary data. SAARC comprises 3% of the world's area, 21% of the world's population and 3.8% (US$2.9 trillion) making up a total of 3% of the world’s area. The country has second in all over the world in terms of agriculture position. The population obliquely all of the member states is over 1.7 billion, accounting for 21% of the world’s total population. In their 42% of the agricultural operation in SAARC nations and also 51% source of livelihood of the South Asians. The study has revealed that India alone accounts for 52 per cent of the agricultural products using the SAARC region peoples. For the present study, a total of 34 groups related to the agricultural products were selected out of the total groups. The techniques employed to analyze the data include descriptive statistic, correlation and linear forecast method. The study also revealed a positive and important relationship between economic growth and foreign direct investment flow to the agricultural sector. Thus, the study recommends that policy should focus on flexible trade policies to attract more foreign direct investment (FDI) inflows to SAARC nations. i.e. Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan, Sri Lanka including India

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

SummaryOne of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

Application of BP Neural Network Model based on Particle Swarm Optimization in Enterprise Network Information Security

The development of network technology has brought convenience to people's life, but also provides the convenience for the virus, Trojan and other destructive programs to attack the network. Then, the computer network security is becoming more and more dangerous. Accurately and scientifically predict the risk of network, it can effectively prevent the risk, and reduce the loss caused by the problem of computer network security. Computer network security is an early warning problem of multi index system. So, the traditional linear forecasting method cannot accurately describe the impact of each index on the evaluation results, and the accuracy of the prediction results is low. In order to improve the prediction accuracy of computer network security, this paper presents a new forecasting method for computer network security. Firstly, the evaluation index of computer network security is selected by expert system, and the weight of evaluation index is determined by the expert scoring method. Secondly, we put the index weight into the BP neural network, and use the BP neural network to learn it. Then, the parameters of BP neural network are optimized by the improved particle swarm optimization algorithm. After that, this paper uses a method based on the Fibonacci method principle to find the number of hidden layer node which has the best fitting ability. Finally, we use this algorithm to predict the network security of a certain enterprise in the next six months. The score is 0.67, 0.84, 0.72, 0.87, 0.86 and 0.91, which is close to the actual value of network security.

Self-organizing map clustering technique for ANN-based spatiotemporal modeling of groundwater quality parameters

The present study integrates co-kriging as spatial estimator and self-organizing map (SOM) as clustering technique to identify spatially homogeneous clusters of groundwater quality data and to choose the most effective input data for feed-forward neural network (FFNN) model to simulate electrical conductivity (EC) and total dissolved solids (TDS) of groundwater. The methodology is presented in three stages. In the first stage, a geostatistics approach of co-kriging is used to estimate groundwater quality parameters at locations where the groundwater levels are measured. In stage two, a SOM clustering technique is used to identify spatially homogeneous clusters of groundwater quality data. The dominant input data, selected by spatial clustering and mutual information are then imposed into the FFNN model for one-step-ahead predictions of groundwater quality parameters at stage three. The performance of the newly proposed model is compared to a conventional linear forecasting method of multiple linear regression (MLR). The results suggest that the proposed model decreases dimensionality of the input layer and consequently the complexity of the FFNN model with acceptable efficiency in spatiotemporal simulation of groundwater quality parameters. The application of FFNN for modeling EC and TDS parameters increases the accuracy of predictions respectively up to 84.5% and 17% on average with regard to the MLR model.

A Novel Nonlinear Integrated Forecasting Model of Logistic Regression and Support Vector Machine for Business Failure Prediction with All Sample Sizes

Abstract The aim of this work was to improve the forecasting performance of business failure prediction with all sample sizes by constructing a novel nonlinear integrated forecasting model (ANIFM) of individual linear forecasting models and individual nonlinear forecasting models. First, a new variable set including internal variables and external variables was proposed. Using scatter diagrams, all variables were placed in either the linear group or the nonlinear group. We considered logistic regression (LR) as the individual linear forecasting method to deal with each linear variable, the support vector machine (SVM) as the individual nonlinear forecasting method to deal with each nonlinear variable, and the residual SVM as the integration method to integrate the forecasts of LRs and SVMs. The proposed procedure was applied to real datasets from China. For performance comparison, single LR, SVM methods, integration forecasting models based on equal weights and on neural networks, and one based on rough set and Dempster-Shafer evidence theory (D-S theory) were also included in the empirical experiment as benchmarks. The experimental results demonstrate the superior forecasting performance of the proposed ANIFM in terms of forecasting accuracy and forecasting stability, especially with small sample sizes.

Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling

Summary Accurate and reliable groundwater level forecasting models can help ensure the sustainable use of a watershed’s aquifers for urban and rural water supply. In this paper, a Self-Organizing-Map (SOM)-based clustering technique was used to identify spatially homogeneous clusters of groundwater level (GWL) data for a feed-forward neural network (FFNN) to model one and multi-step-ahead GWLs. The wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary GWL, runoff and rainfall time series. The performance of the FFNN model was compared to the newly proposed combined WT–FFNN model and also the conventional linear forecasting method of ARIMAX (Auto Regressive Integrated Moving Average with exogenous input). GWL predictions were investigated under three different scenarios. The results indicated that the proposed FFNN model coupled with the SOM-based clustering method decreased the dimensionality of the input variables and consequently the complexity of the FFNN models. On the other hand, the application of the wavelet transform to GWL data increased the performance of the FFNN model up to 15.3% in average by revealing the dominant periods of the process.

Mood dynamics in bipolar disorder.

The nature of mood variation in bipolar disorder has been the subject of relatively little research because detailed time series data has been difficult to obtain until recently. However some papers have addressed the subject and claimed the presence of deterministic chaos and of stochastic nonlinear dynamics. This study uses mood data collected from eight outpatients using a telemonitoring system. The nature of mood dynamics in bipolar disorder is investigated using surrogate data techniques and nonlinear forecasting. For the surrogate data analysis, forecast error and time reversal asymmetry statistics are used. The original time series cannot be distinguished from their linear surrogates when using nonlinear test statistics, nor is there an improvement in forecast error for nonlinear over linear forecasting methods. Nonlinear sample forecasting methods have no advantage over linear methods in out-of-sample forecasting for time series sampled on a weekly basis. These results can mean that either the original series have linear dynamics, the test statistics for distinguishing linear from nonlinear behaviour do not have the power to detect the kind of nonlinearity present, or the process is nonlinear but the sampling is inadequate to represent the dynamics. We suggest that further studies should apply similar techniques to more frequently sampled data.Electronic supplementary materialThe online version of this article (doi:10.1186/s40345-014-0011-z) contains supplementary material, which is available to authorized users.

Stock Price Predicting Using SVM Optimized by Particle Swarm Optimization Based on Uncertain Knowledge

Stock prices have the characteristics of nonlinearity, randomicity and uncertainty, so It is difficult to accurately depict the change rules of stock prices using traditional linear forecasting methods, which lead to low stock price prediction accuracy. In order to improve the stock price prediction precision , this paper proposed a stock price predicting model using SVM optimized by particle swarm optimization based on uncertain knowledge(PSO-UK). We used the great optimization ability of PSO-UK to optimize the parameters of SVM, enhanced the learning ability of SVM , and used the SVM to predict stock price. We compared the accurancies of PSO-UK-SVM and PSO-SVM using SSE Composite Index. Experimental results showed that PSO-UK-SVM model performed better degree of fitting and accuracy. The model proposed in this paper has some guiding significance for investors .

A probabilistic approach to sea level rise up to the year 2100 at Kolobrzeg, Poland

As an example of the linear forecast method, the application of a linear trend to annual mean sea level data from Ko8obrzeg is analysed, and the mean sea level rise for a 110 yr time period is forecast. On the basis of the classical trend theory, the possibility of making a linear forecast on the basis of variable confidence intervals is demonstrated and various computational techniques are compared. The method assumes the application of confidence intervals not only to the stochastic component of the forecast model but also to the estimated trend parameters. As a result, depending on the assumptions and measurement data, the linear forecast method can be adapted to a variety of situations where low-lying areas are under threat from rising sea levels. The possibility of including the mean sea level rise forecast in the probabilistic forecast of storm surges is discussed. Example calculations for the period 1901 to 1990 were performed for a time series of maximum annual sea levels at Ko8obrzeg, estimated by Gumbel's distribution.

Preseason Forecasts of Sockeye Salmon (Oncorhynchus nerka): Comparison of Methods and Economic Considerations

We compared the ability of two methods to forecast observed abundances of sockeye salmon (Oncorhynchus nerka) in Bristol Bay, Alaska, stocks from 1956 through 1980. We determined that the commonly used linear forecasting method, based on untransformed abundances of sibling age classes, overestimated sockeye abundances more frequently than did a nonlinear method. Furthermore, the nonlinear method produced mean forecasts that were closer to actual abundances of returns than forecasts from the linear method. We therefore recommend use of the nonlinear method. We also quantified significant economic implications of incorrect preseason forecasts for Bristol Bay sockeye. Deviations of actual escapements from the target are correlated with errors in the preseason forecast. Thus, large errors in preseason forecasts can influence potential long-term harvests and resulting economic benefits. In addition, annual changes in prices of set and drift gill net permits for fishermen were correlated with forecasted annual change in the abundance of sockeye recruits. This result is consistent with the hypothesis that preseason forecasts of sockeye salmon returns affect expectations by fishermen of economic rents (related to profits). Overestimates of abundance of recruits by current methods may thus subsequently lead to overinvestment by fishermen seeking to increase their share of expected rents.