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The number of open unemployment in South Sumatra Province from year to year is found to be unstable. It can cause serious developmental problems. One solution to this problem is to build an early warning system by predicting the number of open unemployment in the future so that the Regional Government can establish relative policies to anticipate the negative impacts it will have on the environment, economy, social and politics. Therefore, this study discusses the best model to predict the number of unemployed in South Sumatra Province. The methods used to identify the best model are Single Exponential Smoothing (SES), Brown’s Exponential Smoothing (BES), and Holt’s Exponential Smoothing (HES). The Exponential Smoothing methods are compared to obtain forecasting results with a minimal error rate. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics are used to measure the performance of the forecasting model. Empirical results show that the SES model with the smoothing parameter value = 0.7 is the best significant model in predicting the number of open unemployment in South Sumatra Province with a MAPE value of 6.24% and an RMSE value of 23.058. Thus, this SES model can be a reference for the Government to predict the number of open unemployment in South Sumatra Province so that the Regional Government can anticipate the negative impacts it can cause.

The inflation rate, particularly in South Sulawesi Province from year to year, is found to be very unstable, so that an effort to overcome the instability of the inflation rate is highly needed. One of the efforts that can be used is to carry out a process of forecasting the inflation rate, so that the government can predict the inflation rate properly in order to realize the sustainable economic growth. The aim of this study was to forecast Inflation Cases in South Sulawesi Province. The forecasting carried out in this study used the Exponential Smoothing method. Exponential Smoothing is a method that will take into account average (smoothing) the data of the past exponentially by repeating calculations continuously using the latest data. In this study, 2 Exponential Smoothing methods were compared, namely: Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES) which were used to obtain prediction results and evaluate the results of predictions using the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) methods. The smallest MAPE value was obtained when using the Single Exponential Smoothing (SES) method when the value ɑ = 0.1 with the MSE value of 0.5567 and MAPE value of 265.7126 and the Double Exponential Smoothing (DES) method when the value ɑ = 0.3 and with the MSE value of 4,256 and MAPE value of 574,519. Thus, the Single Exponential Smoothing (SES) method was regarded as the best method in predicting the inflation rate in South Sulawesi Province.

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

This paper is based on the exponential smoothing model to enhance the abilities of watercolor students and achieve their stylized watercolor creation. Firstly, the system is structured from the predicted value of the smoothing index. The sum of weights is calculated for each coefficient, and the quadratic, exponential smoothing model is modified. The design of the watercolor teaching system based on the quadratic, exponential model was completed after analyzing the components of the teaching model. Finally, a comparison experiment was conducted to verify that the overall performance of students was improved, and the final test score of painting reached 69%, which was significantly improved relative to that of the control class. The students in the experimental class were also tested for their T-values, and the test results were all below the significance level. It indicates that after the systematic teaching designed in this paper, the watercolor students' watercolor skill ability, personal stylization, and watercolor creation ability have a positive enhancement effect.

Prediction of an organization’s performance has become essential for any organization for its potential customers to place orders with confidence. Overall Equipment Effectiveness (OEE) is one of the acknowledged measures for performance monitoring. In this paper, two different techniques, a simple moving average and Holt’s double exponential smoothing methods, are used to evaluate OEE and to predict the future performance of overall equipment effectiveness in R studio. Holts Double exponential smoothing method was found to result in minimum error measured by mean absolute deviation. Python program is to predict major losses to improve the productivity of the organization by management.

Water scarcity and drought management is the burning issue in India and hence needs serious attention of researchers to develop rigorous plan and management. Areas that belong to various plateaus, e.g., Chotanagpur plateau, Deccan plateau, etc., are mostly affected by drought in India. In the past decade, Bankura District of West Bengal, which belongs to northeast part of Chotanagpur plateau, faced severe drought several times. However, the assessment of drought scenario in this area is far from conclusive statement till date. In this paper, we simulate standardized precipitation index (SPI) using double exponential (DE) and Holt–Winter exponential smoothing model (HW) for several time steps (e.g., 3 months, 6 months, 12 months, 24 months and 48 months) in the time period of 1979–2014. The comparative analysis between two models indicates that DE is more accurate one. DE is observed with relatively low root mean squared error (RMSE) and high R 2 value. Furthermore, drought-prone zones are demarcated using combined scores of principal component analysis (PCA) and those combined scores are estimated using actual, HW and DE simulated SPI in several time steps. At the shorter (3 and 6 months) and longer time step (12, 24 and 48 months), the PCA demonstrates almost same results. The western and northwestern blocks of the district are severely affected by drought, and the southern portions are at mild condition. Spatially distributed RMSE in every time steps is also high in northwestern portions of the study region. Our result may be useful to understand the pattern of drought to take necessary action in management of water resources in Bankura District, West Bengal. Moreover, the study uses an unique methodology to simulate and assess meteorological drought, which is applicable in any region of the world.

This study first analyzes the national and global infection status of the Coronavirus Disease that emerged in 2019 (COVID-19). It then uses the trend comparison method to predict the inflection point and Key Point of the COVID-19 virus by comparison with the severe acute respiratory syndrome (SARS) graphs, followed by using the Autoregressive Integrated Moving Average model, Autoregressive Moving Average model, Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors, and Holt Winter's Exponential Smoothing to predict infections, deaths, and GDP in China. Finally, it discusses and assesses the impact of these results. This study argues that even if the risks and impacts of the epidemic are significant, China's economy will continue to maintain steady development.

CNN originates from image processing and is not commonly known as a forecasting technique in time-series analysis which depends on the quality of input data. One of the methods to improve the quality is by smoothing the data. This study introduces a novel hybrid exponential smoothing using CNN called Smoothed-CNN (S-CNN). The method of combining tactics outperforms the majority of individual solutions in forecasting. The S-CNN was compared with the original CNN method and other forecasting methods such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). The dataset is a year time-series of daily website visitors. Since there are no special rules for using the number of hidden layers, the Lucas number was used. The results show that S-CNN is better than MLP and LSTM, with the best MSE of 0.012147693 using 76 hidden layers at 80%:20% data composition.

Forecasts of prices can help industries in their risk management. This is especially true for Japanese logs, which experience sharp fluctuations in price. In this research, the authors used an exponential smoothing method (ETS) and autoregressive integrated moving average (ARIMA) models to forecast the monthly prices of domestic logs of three of the most important species in Japan: sugi (Japanese cedar, Cryptomeria japonica D. Don), hinoki (Japanese cypress, Chamaecyparis obtusa (Sieb. et Zucc.) Endl.), and karamatsu (Japanese larch, Larix kaempferi (Lamb.) Carr.). For the 12-month forecasting periods, forecasting intervals of 80% and 95% were given. By measuring the accuracy of forecasts of 12- and 6-month forecasting periods, it was found that ARIMA gave better results than did the ETS in the majority of cases. However, the combined method of averaging ETS and ARIMA forecasts gave the best results for hinoki in several cases.

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.