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As a heavy equipment distributor company with a widespread population of units across Indonesia, PT. Kobexindo Tractors Tbk holds a significant spare parts inventory to meet their customers' needs. However, efficiently and effectively managing spare parts inventory is a challenge the company faces. Over the period from 2016 to 2023, the company experienced an average annual loss of Rp. 1,176,438,113, due to the inadequate analysis of spare parts demand, which serves as a reference in the procurement process. To address this issue, this research focuses on developing a model that can generate accurate forecasts for spare parts inventory, particularly Jungheinrich parts, to support appropriate management decisions in the procurement process at the company. The Exponential Smoothing method is chosen for its ability to handle data with fluctuating patterns and trends. Based on a review of previous research studies, the Exponential Smoothing method has proven to be the most effective in forecasting spare parts inventory with a high level of accuracy. This study will compare the Simple Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods. The attributes included in this research are spare parts inventory data collected from the company during the period of 2016-2023, which will be processed using Exponential Smoothing techniques. The results of these calculations will be evaluated using the Root Mean Square Error (RSME) and Mean Absolute Percentage Error (MAPE) techniques to test the accuracy and performance of the forecasting models. The data ratio used in this research is 70% for training data and 30% for testing data. The prototype development is conducted using the Python programming language. The research results indicate that the Holts Winter Exponential Smoothing Model with Multiplicative Seasonality and Multiplicative Trend (Triple Exponential) is the best method among others, as seen from the modeling evaluation results as follows: 1) Train RSME (7.082307), a low RSME value on training data indicates that this model has a small used for training. 2) Test MAPE (6.343268), a low MAPE value on test data indicates that this model provides fairly accurate predictions in percentage terms of the actual values on the test data. 3) Test RSME Values (23.160521), a sufficiently low RSME value on test data indicates that this model also successfully generalizes well on unseen data.prediction error rate on the data [ABSTRACT FROM AUTHOR]

Inflation is a process of continuous increase in the price of goods or an economic situation that shows a tendency to increase the price level in general. One way to control inflation is to use forecasting. Forecasting is an activity to predict future events. Related to forecasting, the method that can be used in forecasting inflation time series data in Indonesia is the fuzzy time series method of Lee's logic. This study aims to apply Lee's Fuzzy Time Series method in forecasting inflation in Indonesia in the period January 2014 to December 2022. The forecasting results of the method are then measured using MAE and MAPE. In this study, the results of inflation forecasting in Indonesia follow the pattern of actual data. Meanwhile, the forecasting accuracy value obtained a MAE value of 0.0043 and a MAPE value of 11.7%, so that inflation forecasting in Indonesia using the Fuzzy Time Series Lee method based on MAPE criteria is good

Crimes are basically committed due to low human faith and ultimately disrupt harmony, including inter-religious harmony. Therefore, the purpose of this study is to predict and determine the number of law violations that occurred in Yogyakarta Province in 2022 as a case study using a linear regression algorithm based on data ranging from 2012 to some of the data on law violations in 2022. The method used is the forecasting method in which the stages of data collection, data preprocessing, data modeling, and prediction processes are carried out. The accuracy of the linear regression model is 90%. However, there is a difference in accuracy between the training data and the test data. The training data gets an accuracy of 99% while the accuracy achieved by the test data is at 94% which is 5% lower than the training data. The prediction results using the linear regression model shows the number of the data on law violation that will occur in 2022 with correlation between crimes.

Earlier, a method for constructing an initial approximation for solving the inverse problem of acoustics by a gradient method based on a convolutional neural network trained to predict the distribution of velocities in a medium from wave response was proposed [9]. It was shown that the neural network trained on responses from simple layered media can be successfully used for solving the inverse problem for a significantly more complex model. In this paper, we present algorithms for processing data about epidemics and an example of applying a neural network for modeling the propagation of COVID-19 in Novosibirsk region (Russia) based only on data. A neural network NN-COVID-19 that uses data about the epidemics is constructed. It is shown that this neural network predicts the propagation of COVID-19 for five days by an order of magnitude better than SEIR-HCD. When a new variant (Omicron) appeared, this neural network was able to predict (after retraining) the propagation of the epidemics more accurately. Note that the proposed neural network uses not only epidemiological data but also social ones (such as holidays, restrictive measures, etc.). The proposed approach makes it possible to refine mathematical models. A comparison of the curves constructed by SEIR-HCD model and by the neural network shows that the plots of solutions of the direct problem almost coincide with the plots constructed by the neural network. This helps refine coefficients of the differential model. [ABSTRACT FROM AUTHOR]

Economic growth in Indonesia affects the need for household furniture. With the increasing demand, consumers have to wait a long time for the requested furniture. This causes sales to not be optimal due to consumer disappointment which results in cancellation of requests. The purpose of this research is to determine the optimal development of sales and make a production schedule based on the forecasting results obtained. The data of this research are window sales at UD. Podomoro Asahan from July 2016 to July 2021. The data was processed using the ARIMA and Winters methods and then compared between the two methods.The results of the study used the ARIMA method, namely using the ARIMA model (0,1,1) with an error value of MPE -0.079772% and MAPE 16.592778%. The results of the study used the Winters method, namely using the smoothing constant α=0.546225; β=0.259846; γ=0.116178; with MPE error value -0.39785% and MAPE 39.78471%.

Objectives: This study developed predictive models for one‐week acute and six‐month persistent pain following root canal treatment (RCT). An additional aim was to study the gain in predictive efficacy of models containing clinical factors only, over models containing sociodemographic characteristics. Methods: A secondary data analysis of 708 patients who received RCTs was conducted. Three sets of predictors were used: (1) combined set, containing all predictors in the data set; (2) clinical set and (3) sociodemographic set. Missing data were handled by multiple imputation using the missing indicator method. The multilevel least absolute selection and shrinkage operator (LASSO) regression was used to select predictors into the final multilevel logistic models. Three measures, the area under the receiver operating characteristic curve (AUROC) and precision‐recall curve (AUPRC) and calibration curves, were used to assess the predictive performance of the models. Results: The selected‐in factors in the final models, using LASSO regression, are related to pre‐ and intra‐treatment clinical symptoms and pain experience. Predictive performance of the models remained the same with the inclusion (exclusion) of the socio‐demographic factors. For predicting one‐week outcome, the model built with combined set of predictors yielded the highest AUROC and AUPRC of 0.85 and 0.72, followed by the models built with clinical factors (AUROC = 0.82, AUPRC = 0.66). The lowest predictive ability was found in models with only sociodemographic characteristics (AUROC = 0.68, AUPRC = 0.40). Similar patterns were observed in predicting six‐month outcome, where the AUROC for models with combined, clinical and sociodemographic sets of predictors were 0.85, 0.89 and 0.66, respectively, and the AUPRC were 0.48, 0.53 and 0.22, respectively. Conclusions: Clinical factors such as the severity and experience of pre‐operative and intra‐operative pain were discovered important to the subsequent development of pain following RCTs. Adding sociodemographic characteristics to the models with clinical factors did not change the models' predictive performance or the proportion of explained variance. [ABSTRACT FROM AUTHOR]

The demand for eggs is always high, but the availability of goods is often not balanced with the availability of products on the market. The imbalance between the demand for eggs and the supply makes the price unstable. Chicken eggs are a food commodity that has variable price fluctuations. . Therefore an estimate of future prices is required. Various models can be used as a method of forecasting egg prices. The study was conducted to determine and test the best forecasting model for the retail price of eggs in Bengkulu City. The retail selling value of chicken eggs in the capital city of Bengkulu Province from early 2010 to May 2020 was used as the main variable to be analyzed. ima price forecasting model used to determine the best model based on the lowest MAD, MSE and MAPE values. The results showed that the best model for predicting the retail price of eggs in Bengkulu City is the ARIMA model because it has the smallest MAPE, MAD and MSE values compared to other models.

In this paper, the Box-Jenkins methodology has been applied and ‎used to forecast the ratio of Iraq's ‎rural population from 1960 to 2019. A sample size of (60) observations of the ‎annually rural population ‎of Iraq has been taken. A combination of ‎some adequate time series models has been prepared and ‎‎obtained and some statistical criteria have been used for comparison and model selection. Results of ‎the study concluded ‎that the ARIMA (0,2,1) is an adequate and best model to be used for ‎forecasting ‎the annual ratio of rural population data in Iraq. ‎During the period 2020 to 2030, the ratio of the rural ‎population ‎will keep decreasing gradually, and the percentage of the rural ‎population of Iraq in 2030 ‎will be (27.732).‎

Three predictive problems bedevil our ability to foresee political crises and state breakdown: (1) how to tell when a previously stable state falls into a situation of hidden but dangerous instability; (2) how to tell, once a certain level of instability has appeared in the form of protests, riots, or regional rebellions, whether chaos will grow and accelerate into revolution or civil war, or if the protests are likely to be contained and dampen out; and (3) how to tell which individuals and groups are likely to be the main source of mobilization for radical movements, and whether opposition networks will link up, grow and spread, or be isolated and contained. Prior work has focused on each of these problems separately. However, all three issues are crucial to understanding and foreseeing conflict dynamics. These issues operate on different time-scales and require separate models. In this article we discuss how better models of each process could be developed and, crucially, integrated with data for a more effective prediction system. A major theoretical challenge for us is to link these different approaches in order to increase their predictive power. A major empirical challenge is to identify data (direct or proxy) that can be used to parameterize, validate, and test our models.

Inventory is an important issue to control. Both finished goods and raw material inventories are the same with ingredient raw for the production process . no seldom our find problem where company no To do good control in management inventory that results in needs customer no fulfilled. Ray Traso Jaya is company that manufactures and sells paving block products, bataco, riol concrete and pits wind where in the production process experience disturbance in carry out the production process caused level management supply ingredient raw sand and cement that are not optimal as a result company difficulty for Fulfill needs customers who are fluctuating . Formulas problem in study this is " How " determine supply ingredient raw materials and costs supply ingredient optimal raw material at Sinar Traso Jaya?". Study this aim for determine supply ingredient optimal raw material and cost supply ingredient optimal raw . Method analysis used in research this is method Analysis Calculation With Markov Chain. Where is the result study this company can adapt costs required at the time level booking with ingredient fluctuating standard with notice stock ingredient available raw. That is if ingredient raw sand initial 0 or stock empty on level ordering 170.638 kg the optimal cost is at Rp. 1300,000, if ingredient cement raw 0 or stock empty on level booking as much as 57,617 kg with the optimal cost is IDR 1,300,000. [ABSTRACT FROM AUTHOR]

Introduction: Material Requirement Planning (MRP) is a technique or a logical procedure to translate the Master Production Schedule (MPS) of the finished goods or end item into the net requirements for some of the components needed to implement the MPS. MRP is used to determine the amount of material needs to support the Master Production Schedule and when the material needs to be scheduled. Methods: The study is conducted on 13 August 2018 until 12 September 2018 at the installation Nutrition RSIA Kendangsari Merr Surabaya. Collecting data in this study using several methods, including: observation- This stage is conducted in all parts related to the object of study, starting from the Purchase Order (PO) by a head cook up to raw material stored in the storage, discussion- author interviews and collects data to communicate and discuss with the respondents. Respondents in this study are the head of the nutrition unit and head cook of RSIA who have the authority doing the purchasing. Results: Planning of procurement of raw materials to the menu rawon in RSIA can use the Exponential Smoothing method. The discussion has compared two methods of forecasting and the results are consistent with the demand’s patterns of Simple Moving Average method, Exponential Smothing. Forecasting has the lowest error rate by using Exponential Smoothing. The second conclusion is a technique of determining the appropriate Material Requirement Planning in raw material procurement to menu rawon in RSIA is using Lot for Lot.

The dependence of community consumption pattern on rice has caused high demand for rice from year to year. These conditions have an impact on the decreasing of local resources diversity. This is because farmers prefer to grow rice. This study aims to explain problems in diversifying food consumption based on local resources and its policy in Indonesia. This is a literature study based on scientific articles related to the issues for 10 years. Result shows that the main problem of food diversification is the imbalance between the pattern of food consumption and the provision of food production and its availability in the community. The Indonesian government has not been sufficiently committed to use the concept of food diversity to replace the consumption patterns of its majority community in rice. Food diversification needs to be developed through the development of various value-added food industries. Large partnerships among stakeholders need to be done by involving all stakeholders, namely local government, research institutions, industry, universities, NGOs and the community.

Deep machine learning algorithms play an important role in facilitating the development of predictive models for the stock market. However, most studies focus on predicting next-day stock prices or movements, limiting the usability of the predictive model for investors. This study extensively explores the ability of deep learning models to predict out-of-sample the daily prices of global stock indices over a long term, up to a year. The performance of six models, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), are compared using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The models predict the long-term daily prices of five global stock indices, namely the Nifty, the Dow Jones Industrial Average (DJIA), the DAX performance index (DAX), the Nikkei 225 (NI225), and the Shanghai Stock Exchange composite Index (SSE). The results confirm the superiority of LSTM for predicting long-term daily prices. The Bi-LSTM does not improve the result of LSTM but performs better than other algorithms. CNN overfits the training data and poorly forecasts the long-term stock prices of global indices on the testing data. This research demonstrates the potential of deep learning models for long-term stock price forecasting, offering valuable insights for investors. Additionally, the patterns of predicted daily prices can be helpful in building trading and risk management decision systems. [ABSTRACT FROM AUTHOR]

This paper aims to clarify the main issues concerning the role of incomes and expenditures budget as the main tool for achieving the forecast and pursuit the equilibrated financial standing of a company. In this context the main aims to be achieved are presented through the incomes and expenditures budget and its contents and structure as well as the main forms that they take in this budget within a company.

With the rapid development of the Internet and data-processing technologies, Internet sentiment analysis can be used to explore many possibilities, from Internet news about products or the influence of product price to the influence of sale behaviour and important brand strategies. In this paper, we analyse news affecting the price of products, and establish a new model for price prediction. The results show that significant news events have an impact on the sale prices of electronic products, and can improve the accuracy of price forecasts. Thus, the contribution of this paper is to propose a new forecasting model for the price of e-commerce products. [ABSTRACT FROM AUTHOR]

Forecasting is an essential capability for most firms. This suggests that there may be a demand for graduates that understand forecasting. The authors of this study review extant literature and analyze data from a sample of marketing faculty to better understand the role of forecasting in the marketing curricula. This study examines the perceptions of marketing faculty regarding: (1) the need for competency in forecasting, (2) the importance of various forecasting techniques, (3) the extent to which faculty are satisfied with forecasting learning outcomes, (4) the "ownership" of forecasting in academe and industry, and (5) the marketing courses in which forecasting should be taught. The authors found that in marketing, forecasting is rarely taught as a standalone course and make literature-based suggestions for more effectively integrating forecasting into the marketing curriculum. [ABSTRACT FROM AUTHOR]

This paper aims to clarify the main issues concerning the role of incomes and expenditures budget as the main tool for achieving the forecast and pursuit the equilibrated financial standing of a company. In this context the main aims to be achieved are presented through the incomes and expenditures budget and its contents and structure as well as the main forms that they take in this budget within a company. [ABSTRACT FROM AUTHOR]

The paper includes an analysis of methods of diagnosis and prediction of the enterprise crisis in order to anticipate and successfully overcoming them. Have been reported a number of shortcomings and deficiencies of several methods of crisis management at the enterprise, respectively, was argued the need to develop new diagnostic and forecasting models not only for the bankruptcy but for overall crisis. In this respect, we proposed a model of diagnosis / prediction of the financial crisis (bankruptcy) for entreprises from emerging countries, based on the statistical analysis method, namely the multifactorial discriminant. [ABSTRACT FROM AUTHOR]

Product lifecycles are becoming shorter and shorter because of the global competition. In such highly competitive condition, the importance for enterprises to build forecasting models with short-term time series data significantly arises in recent decade. Unfortunately, since the information the short-term data provides is limited, it is a great challenge to learn its pattern behavior. To overcome the issue, the Grey Model (GM) reveals a clear direction by accumulating data values to make them represent as a monotonically increasing sequence. Accordingly, improving the forecasting accuracy of GM has remarkable developments in theories in decades. Among them, the Adaptive Grey Model (AGM) tries to find the suitable background values by means of taking the data occurring trend into consideration. In fact, setting the suitable background values of GM can be regarded as a process of searching the optimal solutions. This paper thus employs the genetic algorithm (GA) to facilitate the improvement of searching process, where the initial solutions are obtained from AGM, to build a more accurate model named as GAAGM(1,1). The experiment results show that GAAGM outperforms AGM and some other improved GM models in prediction accuracy; and furthermore, this demonstrates that searching for more suitable coefficient sets can indeed help enhance the theory structure of GM models. [ABSTRACT FROM AUTHOR]

This Project evaluates the out-of-sample forecasting accuracy of winter models for 5 month period from April 2013 to August 2013 Sensex in BSE. Sensex is defined as within-month as alpha & beta of Sensex on the BSE for the 12month period April 2012 to march 2013. [ABSTRACT FROM AUTHOR]

Forecasting is an important activity in finance. Traditionally, forecasting has been done with in-depth knowledge in finance and the market. Advances in computational intelligence have created opportunities that were never there before. Computational finance techniques, machine learning in particular, can dramatically enhance our ability to forecast. They can help us to forecast ahead of our competitors and pick out scarce opportunities. This paper explains some of the opportunities offered by computational intelligence and some of the achievements so far. It also explains the underlying technologies and explores the research horizon. [ABSTRACT FROM AUTHOR]

Abstract: This paper reformulates the Volland–Stern model, separating contributions from corotation and convection to predict electric field penetration of the inner magnetosphere using data from the Advanced Composition Explorer (ACE) satellite. In the absence of shielding, the model electric field is E VS=Φ PC/2L Y R E, where Φ PC is the polar cap potential and 2L Y R E is the width of the magnetosphere along the dawn–dusk meridian. Φ PC is estimated from the interplanetary electric field (IEF) and the dynamic pressure of the solar wind (P SW); values of L Y were approximated using P SW and simple force-balance considerations. ACE measurements on 16–17 April 2002 were then used to calculate E VS for comparison with the eastward electric field component (E Jφ ) detected by the incoherent scatter radar at Jicamarca, Peru. While the interplanetary magnetic field (IMF) was southward, the model predicted observed ratios of E VS/IEF. During intervals of northward IMF, EJφ turned westward suggesting that a northward IMF B Z system of field-aligned currents affected the electrodynamics of the dayside ionosphere on rapid time scales. [Copyright &y& Elsevier]

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation! This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability: The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys. [ABSTRACT FROM AUTHOR]

Pollen-related allergy is a common disease resulting in symptoms of hay fever and asthma. Control of symptoms depends (generally) on avoidance and pharmacological treatment. Both of these approaches could benefit from accurate predictions of pollen levels for future days. We have constructed a model that uses meteorological data to predict ragweed pollen levels based on air samples collected daily in Kalamazoo, MI from 1991 to 1994. Ragweed pollen counts were converted to pollen grains/m3 of air (24-h average). We used Poisson regression, which appropriately handles the heterogenous variance associated with pollen data. Using standard statistical model selection procedures, combined with biological considerations, we selected rainfall, wind speed, temperature, and the time measured from the start of the season as the most significant variables. Using our model, we propose a method that uses the weather forecast for the following day to predict the ragweed pollen level. This approach differs from most previous attempts because it uses Poisson regression and because this model needs to be fit iteratively each day. By updating the coefficients of the model based on the information to date, this method allows the fundamental shape of the pollen distribution curve to change from year to year. Application to the Kalamazoo data suggests that the method has good sensitivity and specificity for predicting high pollen days. [ABSTRACT FROM AUTHOR]