Price Forecasting of Feed Raw Materials Used in Dairy Farming: A Methodological Comparison

Forecasting multicycle hollow fiber ultrafiltration fouling using time series analysis

Forecasting performance of grey prediction for education expenditure and school enrollment

This study aimed to explore the epidemiological characteristics of breast cancer and establish an Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) models to predict the development of incidence in Shantou. This study has a large sample size, strong representativeness, and wide‐ranging and comprehensive medical insurance information, which can fill the gaps in basic epidemiological research on breast cancer in Shantou. Successful completion of this study is a helpful tool to understand the epidemiology of Guangdong Province and Southern China. This study also provides data and scientific references for the government and future research on breast cancer prevention and control. This retrospective study was conducted to describe the epidemic intensity, epidemic distribution, and epidemic trend of breast cancer in Shantou, Guangdong Province, from 2006 to 2017, gathered from the Shantou's Medical Security Bureau covers the whole districts of Shantou. ETS and ARIMA models were used to describe the regional distribution, time distribution, and population distribution of breast cancer in Shantou. Moreover, based on the ARIMA model and ETS model, the incidence trend of breast cancer was predicted during 2018–2022. This study included 5,681 breast cancer patients, majority of whom were aged 50–59 years. The male‐to‐female ratio of the breast cancer patients was about 1:107 (the same ratio of the insured population was 1:1). Female patients accounted for 98.61% of the total insured population. The incidence and mortality rates of female breast cancer were 16.42/100,000 and 0.66/100,000, respectively. Based on the ARIMA model or ARIMA and ETS model, a gradually decreasing trend in the incidence of breast cancer is expected in the future. Comparing the performances of the ARIMA model and ETS model, ARIMA (4, 0, 1) (0, 1, 0) model had a lower the root mean squared error and the mean absolute percentage error than ETS (M, N) model. This population‐based retrospective study showed that the high‐risk age for the age‐specific incidence of female breast cancer was 50–55 years. It is recommended that healthcare administration should strengthen program awareness and education regarding breast cancer prevention and control. It is also possible that feasibility of extrapolating the current methodology to other future studies or broader populations in which the cancer registry data are not available.

Dairy Farmers’ Strategies against the Crisis and the Economic Performance of Farms

Purpose – The purpose of this paper is to synthesize the review of the existing literature attached to the grey economic system theory and applications and aims to offer a comprehensive picture of the contribution brought by the researchers to this particular field. Also, the paper underlines the main research areas within the grey economic theory and applications and serves as an informative summary kit for future research works and research directions. Design/methodology/approach – For appreciating the scientific progress made since the grey systems theory has been initiated to the present, with an accent on the literature dedicated to the economic field, a bibliometrics analysis has been conducted. The Perish or Publish software was used for extracting the needed data from Google Scholar for the entire period since the appearance of grey systems to now-a-days. In addition, an ISI Web of Science (WoS) search has been performed for extracting the grey economic papers. As the main focus is on the economic subject area of the grey systems, only the papers related to this field have been selected. Findings – The total number of grey economic paper from both Google Scholar and ISI WoS database, the number of authors, some citation metrics, H-index, authors’ provenience country, papers’ language, etc., have been presented and analysed. Also, a list with the most cited papers in the grey economic relational analysis, grey economic prediction models and grey economic incidence is putted forward. Practical implications – Through the bibliometric analysis on grey economic papers written over time, a qualitative analysis was performed on this field in order to underline the main research direction, to analyse what has been done in this field and to determine which can be the next research directions that can emerge from here. Originality/value – The paper succeeds in enlarging the view regarding the usage of grey systems theory in the economic field, offering a suitable analysis on the considered areas. Even though bibliometrics analysis have been conducted on the grey systems theory field, a grey economic bibliometric analysis has not been done yet, to the authors’ knowledge. Therefore, a synthesized of the existing literature attached to the grey economic system theory and applications is presented in order to offer a more comprehensive picture of the contribution brought by the researchers to this particular field.

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

High-accuracy and dependable prediction of the bias of space-borne atomic clocks is extremely crucial for the normal operation of the satellites in the case of interrupted communication. Currently, the clock bias prediction for the Chinese BeiDou Navigation Satellite System (BDS) remains still a huge challenge. To develop a high-precision approach for forecasting satellite clock bias (SCB) in allusion to analyze the shortcomings of the exponential smoothing (ES) model, a modified ES model is proposed hereof, especially for BDS-2 satellites. Firstly, the basic ES models and their prediction mechanism are introduced. As the smoothing coefficient is difficult to determine, this leads to increasing fitting errors and poor forecast results. This issue is addressed by introducing a dynamic “thick near thin far (TNTF)” principle based on the sliding windows (SW) to optimize the best smoothing coefficient. Furthermore, to enhance the short-term forecasted accuracy of the ES model, the gray model (GM) is adopted to learn the fitting residuals of the ES model and combine the forecasted results of the ES model with the predicted results of the GM model from error learning (ES + GM). Compared with the single ES models, the experimental results show that the short-term forecast based on the ES + GM models is improved remarkably, especially for the combination of the three ES model and GM model (ES3 + GM). To further improve the medium-term prediction accuracy of the ES model, the new algorithms in ES with GM error learning based on the SW (ES + GM + SW) are presented. Through examples analysis, compared with the single ES2 (ES3) model, results indicate that (1) the average forecast precision of the new algorithms ES2 + GM + SW (ES3 + GM + SW) can be dramatically enhanced by 49.10% (56.40%) from 5.56 ns (6.77 ns) to 2.83 ns (2.95 ns); (2) the average forecast stability of the new algorithms ES2 + GM + SW (ES3 + GM + SW) is also observably boosted by 53.40% (49.60%) from 8.99 ns (16.13 ns) to 4.19 ns (8.13 ns). These new coupling forecast models proposed in this contribution are more effective in clock bias prediction both forecast accuracy and forecast stability.

Purpose – The purpose of this paper is to upgrade the collaborative emergency ability of government in the tier of towns, realizing emergency resource share, emergency cost reduction and emergency efficiency improving. This paper mainly aims to solve the problem of forecasting the natural disaster happening year of every township collaborative region in Fangshan District. Design/methodology/approach – First, classify the townships into five collaborative regions through grey clustering. Second, set up a grey disaster forecast model for the whole Fangshan District according the annals of disaster from 1985 to 2012, and forecast the disaster grade. Third, build a grey disaster forecast model for the collaborative regions after constructing the buffer operators of catastrophic sequence according the annals of disaster from 1949 to 2012. Findings – The authors forecasted the happening year and flood grade of future disaster for the whole Fangshan District. The accurate degrees of both flood and drought year model are greater than 90 per cent. The accurate degree of insects calamity year is a little low, but the relative errors are all lower than 3 per cent in recent continuous three times, so in the whole, it can be used. For the collaborative regions, the authors forecasted the future disaster years of them. The accuracy rate of every model is greater than 90 per cent. The result shows that the forecast errors are acceptable. Research limitations/implications – In the models, for the purpose of good accuracy, the authors used different initial data. For example, in the forecast model for whole Fangshan District disaster year, the authors used the data from 1985 to 2012, while in the forecast model for the disaster grade of it, the authors used the data from 1949 to 2012. In the disaster year forecast model for collaborative region, the authors also used the data from 1949 to 2012. If the authors can find a model that has high accuracy rate by using all the date information, it will be better. Practical implications – Township is the most basic level of government organization in China, researching on collaborative emergency in township will do help to take targeted precautions measures against calamity according to the characteristic there. At the same time realizing emergency cost reduction and emergency efficiency improving based on the advantages of emergency resource share, short rescue distance, little effects of communication destruction. Social implications – Because of the stochastic occurring of disasters, it is very important to forecast the happening time of disasters accurately. This paper forecasted the natural disaster happening time of Fangshan District through grey catastrophic model, aimed at giving decision support to related department and strengthen the disaster prevention power targetedly. Originality/value – It is well known that the greater the system, the steadier it is, and the easier to forecast it. Fangshan District, Beijing, is a medium-sized and small system in regional research, while townships are small systems. It is rarely a big challenge for the authors to forecast the disaster years in Fangshan and its collaborative townships. In this paper, the authors used grey system model and Markov transfer matrix in forecasting the disaster years and the disaster grade of flood in Fangshan District. All of them are new trying to using grey system theory in disaster forecast for Fangshan District, Beijing.

PurposeAs the grey systems theory has been widely used in the field of sustainable development (SD) research, in the following, a short literature overview will be put forward, starting from the usage of these theories in the economic development, social inclusion and environmental protection contributions to the evolving process of SD during 2011–2021. The purpose of this paper is to identify some key studies from all the SD areas in which the grey systems can be used in order to open and to bring the researchers to new domains in which they can reveal their interest and in which they can successfully use the methods offered by the grey systems theory.Design/methodology/approachUsing the search engine offered by the Google Scholar and the Web of Science (WoS), a literature review has been performed for the grey systems applications on SD research on both grey relational analysis (GRA) and grey forecasting. In addition, some grey evaluation theories – clustering evaluation models and grey target decision models – have also been presented.FindingsMany grey models are widely used in the field of SD. Compared with other methods such as grey prediction, grey evaluation and decision-making model, GRA technology is the most used method, and the research using this method is more than three times that of all other methods.Research limitations/implicationsThe present paper identifies some of the most representative examples in which the grey system theory (GST) has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.Originality/valueThe present paper focuses on the SD applications in which GST has been successfully used, bringing to the reader a general overview on this field and, in the same time, enables new research perspectives.

Grey forecast can master the developing law of system through dealing with incomplete information of system at present. On the basis of actual data of Feng Feng Coal Mine, the grey forecasting model for coal mine accidents due to human factor in Feng Feng by using the grey system theory in this paper, it is shown that models which are built have good precisions. Safety and production of Feng Feng Coal Mine are forecasted by using grey forecasting models which are built. The results show that the forecasting models will help coal mines to forecast accidents due to human factor next year and generally tally with development tendency of Feng Feng Coal Mine.

To address the escalating challenge of food scarcity and the associated conflicts between human and animal consumption, it is imperative to seek alternative resources that can substitute for traditional feed. Non‐grain feed (NGF) raw materials represent a category of biomass resources that are distinct from grains in their composition. These materials are characterized by their high nutritional content, cost‐effectiveness, ample availability, and consistent supply, which contribute to their significant economic potential. Nonetheless, the extensive application of NGF is currently hindered by several limitations, including a high concentration of antinutritional factors, suboptimal palatability, and an offensive odor, among other shortcomings. The synergistic fermentation of probiotics and enzymes (SFPE) is an innovative approach that integrates the use of a diverse array of enzymes during the feed fermentation process, as well as various strains of probiotics throughout the feed digestion process. This method aims to enhance the nutritional value of the feed, diminish the presence of antinutritional factors, and improve the overall palatability, thereby facilitating the optimal utilization of NGF. This strategy holds the promise of not only replacing conventional feed options but also mitigating the pressing issue of grain scarcity. This paper delves into the practical applications of NGF and presents an overview of the latest research advancements in SFPE fermentation techniques, which can provide cutting‐edge and valuable reference for researchers who devote themselves to research in this field in the future.

In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

BackgroundTuberculosis (TB) remained one of the world’s most deadly chronic communicable diseases. Future TB incidence prediction is a benefit for intervention options and resource-allocation planning. We aimed to develop rapid univariate prediction models for epidemics forecasting employment.MethodsThe surveillance data regarding Taiwan monthly TB incidence rates which from January 2005 to June 2017 were utilized for simulation modelling and from July 2017 to December 2020 for model validation. The modeling approaches including the Seasonal Autoregressive Integrated Moving Average (SARIMA), the Exponential Smoothing (ETS), and SARIMA-ETS hybrid algorithms were constructed and compared. The modeling performance of in-sample simulating training sets and pseudo-out-of-sample validating sets were evaluated by metrics of the root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE).ResultsA total of 191,526 TB cases with a highest incidence rate in 2005 (72.5 per 100,000 person-year) and lowest in 2020 (33.2 per 100,000 person-year), from January-2005 to December-2020 showed a seasonality and steadily declining trend in Taiwan. The monthly incidence rates data were utilized to formulate these forecasting models. Through stepwise screening and assessing of the accuracy metrics, the optimized SARIMA(3,0,0)(2,1,0)12, ETS(A,A,A) and SARIMA-ETS-hybrid models were respectively selected as the candidate models. Regarding the outcome assessment of model performance, the SARIMA-ETS-hybrid model outperformed the ARIMA and ETS in the short term prediction with metrics of RMSE, MAE MAPE, and MASE of 0.084%, 0.067%, 0.646%, and 0.870%, during the pseudo-out-of-sample forecasting period. After projecting ahead to the long term forecasting TB incidence rates, ETS model showed the best performance resulting as a 41.69% (range: 22.1–56.38%) reduction of TB epidemics in 2025 and a 54.48% (range: 33.7–68.7%) reduction in 2030 compared with the 2015 levels.ConclusionThis time series modeling might offer us a rapid surveillance tool for facilitating WHO’s future TB elimination milestone. Our proposed SARIMA-ETS or ETS model outperformed the SARIMA in predicting less or 12–30 months ahead of epidemics, and all models showed better in short or medium-term forecasting than long-term forecasting.

In this study, based on the characteristics of the ARIMA models and ETS models, respectively, that the former focuses more on autocorrelation between data, while the latter focuses more on trends and seasonality of data sets. These two forms of models are used to forecast the USD/CNY exchange rate. This study used the monthly average USD/CNY exchange rate from January 2010 to June 2023, which taken from the website of the China Foreign Exchange Trade System (CFETS) , which data is provided by People's Bank of China, and used computer software to forecast and test the results using each model. Ultimately, it was found that the forecasts using the Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) models, were close to each other, with both showing a flattening trend over the long term. In the short term, ETS(M, Ad, N) forecasts an upward trend in the USD/CNY exchange rate while ARIMA(0,1,2) forecasts an almost flat trend. ARIMA (0,1,2) forecasts that the USD/CNY exchange rate shown the shape that finally stabilized at around 6.85, while ETS (M, Ad, N) forecasts around 7.6, and comparatively ARIMA model gives more reliable forecasts on the test set, and fit the training set better. This study compares the accuracy of the ETS model and the ARIMA model in fitting and subsequently forecasting the USD/CNY exchange rate for the period 2010-2023. Future research could build on this by discussing the impact of particular specific events and policies on the forecasts at this stage and experimenting with optimization.

The majority of the existing studies on timber price forecasting are based on ARIMA/SARIMA autoregressive moving average models, while vector autoregressive (VAR) and exponential smoothing (ETS) models have been employed less often. To date, timber prices in primary timber markets have not been forecasted with ANN methodology. This methodology was used only for forecasting lumber futures. Low-labor-intensive and relatively simple solutions that can be used in practice as a tool supporting decisions of timber market participants were sought. The present work sets out to compare RBF and MLP artificial neural networks with the Prophet procedure and with classical models (i.e., ARIMA, ETS, BATS, and TBATS) in terms of their suitability for forecasting timber prices in Poland. The study material consisted of quarterly time series of net nominal prices of roundwood (W0) for the years 2005–2021. MLP was found to be far superior to other models in terms of forecasting price changes and levels. ANN models exhibited a better fit to minimum and maximum values as compared to the classical models, which had a tendency to smooth price trends and produce forecasts biased toward average values. The Prophet procedure led to the lowest quality of projections. Ex-post error-based measures of prediction accuracy revealed a complex picture. The best forecasts for alder wood were obtained using the ETS model (with RMSE and MAE values of approx. 0.38 € m−3). ETS also performed well with respect to beech timber, although in this case BATS was just as good in terms of RMSE, while the difference between ETS and neural models amounted to as little as 0.64 € m−3. Birch timber prices were most accurately predicted with BATS and TBATS models (MAE 0.86 € m−3, RMSE 1.04 € m−3). The prices of the most popular roundwood types in Poland, i.e., Scots pine, Norway spruce, and oaks, were best forecasted using ANNs, and especially MLP models. Among the neural models for oak (MAE 4.74 € m−3, RMSE 8.09 € m−3), pine (MAE 2.21 € m−3, RMSE 2.83 € m−3), beech (MAE 2.31 € m−3, RMSE 2.70 € m−3), alder (MAE 1.88 € m−3, RMSE 2.40 € m−3), and spruce (MAE 2.44 € m−3, RMSE 2.58 € m−3), the MLP model was the best (the RBF model for birch). Of the seven models used to forecast the prices of six types of wood, the worst results were obtained for oak wood, while the best results were obtained for alder.