forecasting in data mining

Sci. Timeframe: Descriptive data mining is focused on analyzing historical data. 1) For PM10 forecasting in the first season, the optimal hybrid models are DEGWO-SVM and DEGWO-BPNN, with which the MAPE values of the best hybrid model (DEGWO-SVM) for four cities of Category III are 0.71%, 0.81%, 1.09%, and 0.72%. Opposition-based Multi-Objective Whale Optimization Algorithm with Global Grid Ranking. (2019). What Is Data Mining? How It Works, Techniques & Examples doi:10.1016/j.scitotenv.2014.07.051, Keywords: air quality index, data analysis, data mining, artificial neural networks, model selection, Citation: Huang Y, Deng Y, Wang C and Fu T (2021) Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants. However, authors have clearly mentioned that the outliers are rejected based on a global view, where extreme values are considered as outliers. Forecasting result of NO2 for three categories in the first season. Mali has revised its 2023 industrial gold forecast to 67.7 t, up from a previous forecast of 63.9 t, according to mines ministry data shared with Reuters on Wednesday. Analysis and Forecasting of the Particulate Matter (PM) Concentration Levels over Four Major Cities of China Using Hybrid Models. Initialize crossover probability Pc and scaling factor F; Evaluate f for all individuals in the parent population; Sort the parent population in a non-decreasing order, according to the objective function value; X is the best individual in the parent population of gray wolves; X is the second individual in the parent population of gray wolves; X is the third individual in the parent population of gray wolves; for each individual in the parent population of gray wolves. Then the competition selection operation is performed according to Eq. Alanazi M, Alanazi A, Khodaei A (2017) Long-term solar generation forecasting. In (Marino et al. Organic, economic, and seasonal factors all influence agricultural yield. The calculation formula is as follows: For the first forecasting, the 1st to 840th samples are the training samples, the 841st to 1008th samples are the testing samples, and the 1009th sample is the forecasting value. 11. Pollut. Instead, we will use freely avail- able benchmark data for testing future energy forecast models which makes the comparison between approaches easier to understand. (2018) applied the RIMA model to predict the concentration of PM2.5 based on time series air quality data covering two warm periods and two cold periods and concludes that PM2.5 concentration is higher in the cold period and lower in the warm period. The result of two types of SVM for three main air pollutants in different cities. doi:10.1016/j.scitotenv.2018.08.315, Shenfield, A., and Rostami, S. (2015). In this section, the specific information of experiment datasets in BJ-TJ-HE are described in detail. doi:10.1016/j.jenvman.2017.03.046. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 1. In addition, 7 of the 10 most polluted cities in the world are in China. Hao, Y., and Tian, C. (2018). The input variables and prediction horizon affect the accuracy of the prediction model. Tech Rep IEA PVPS:T14T01. Where pmeas represents actual solar power generation at ith time step, ppred is the corresponding solar power generation estimated by forecasting model, N is the number of points estimated in the forecasting period. Comparison of ANN (MLP), ANFIS, SVM, and RF Models for the Online Classification of Heating Value of Burning Municipal Solid Waste in Circulating Fluidized Bed Incinerators. Apr 18, 2021 -- 6 Physicists define climate as a complex system. IEEE Trans. data mining They used meteorological data and estimated generated power to train the GRNN and FFBP. Flowchart of forecasting process based on predictive data mining techniques. Algorithm 1 MMODAInput: Objective function Min fitness(x)=f1+f2, Note: Ei is the test error; the calculation equation is Ei=x^ixi, x^i and xi are the actual data and output data by each model. Short-Term Load Forecasts Using LSTM Networks. doi:10.1016/j.apr.2018.06.011, Han, Y., Lam, J. C., Li, V. O., and Reiner, D. (2021). Impacts of Haze Pollution on China's Tourism Industry: A System of Economic Loss Analysis. Data mining Forecasting time series - IBM rand(0, 1) represents a random number in [0, 1]. Environ. 47, 101471. doi:10.1016/j.scs.2019.101471, Liu, Q., Wu, L., Xiao, W., Wang, F., and Zhang, L. (2018). Among the four models, MODEGWO-SVM and MODEGWO-BPNN have better forecasting performance, with the MODEGWO-SVM obtaining 64.29% optimal forecasting points and the MODEGWO-BPNN obtaining 21.43% optimal points for six cities in Category II. The final forecasting results are obtained by model selection, in which the forecasting accuracy is better than those of the four hybrid models. 2016), as both have a direct impact on the forecasting model performance. TABLE 9. What is Forecasting in Data Mining - Javatpoint Kumar, K., Zindani, D., and Davim, J. P. (2019). 8, 103208. doi:10.1201/9781351049580-3, Land, W. H., and Schaffer, J. D. (2020). 3) For the forecasting results of model selection, Table 6 and Figure 4 clearly show that the forecasting performance of model selection is better than the hybrid model. Privacy The air quality data sequence usually has characteristics such as non-stationarity and nonlinearity; thus, the multi-objective optimization algorithm is a suitable choice. doi:10.1016/j.egypro.2015.11.796. Energy forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. Data setting: Each main air pollutants time series can be divided into three parts: training sample and testing samples for the forecasting values. 42, 83318340. A few data mining tools and techniques include: Descriptive modeling The goal of a typical data mining project is to use the mining model to make predictions. ScienceDirect is a registered trademark of Elsevier B.V. ScienceDirect is a registered trademark of Elsevier B.V. https://doi.org/10.1016/j.gltp.2021.08.008. Data mining is the process of analyzing large amounts of data in order to identify patterns, anomalies and correlations. Time Series TABLE 2. 18XTJ003). Pol. In this paper, we utilized neural networks, Nave Bayes, random forest, and K-nearest neighbor algorithms to build weather forecasting prediction models. To address the research questions, we first propose to conduct a case study that aims to benchmark the anomaly detection method and evaluate the link between forecasting accuracy and anomaly detection method. Sci. 244, 118556. Proced. 150, 175197. 2. It is very important for grid operators and decision makers to know how much power RES will produce over next hours and days (Dobschinski et al. Correspondence to In the domain of energy production forecasting, there are several studies which reveal the potential of Artificial Intelligence (AI). For the unknown samples, the classification effect is very poor. The author read and approved the final manuscript. (2008). Big data analytics is the process of analyzing big data to extract the concealed patterns and applicable information that can yield better results. 10, outputs the multi-objective function value of the global optimal X; otherwise, let t=t+1, and then go to Step 3 to continue execution. The number of input layers from 1 to 10 increases for three main air pollutants, which means there are 1,008 pieces of sample data on NO2, PM2.5, and PM10; the train-to-verify ratio 5:1 means that 840 pieces of sample data were used as training data for building the ANN model, while 168 pieces of sample data were used as testing data for finding the training-to-testing ratio and parameter of each ANN model (the optimal number of input layers of each model and the number of hidden layers of LSTM and BPNN). (2019). Overall, the proposed model selection forecast system exhibits outstanding performance in data analysis and time series forecasting for air pollutants. With more and more data available from sources as varied as social media, remote sensors, and increasingly detailed reports of product movement and market activity data mining offers the tools to fully exploit Big Data and Proced. Total Environ. Energies 8:11381153, Filik UB, Gerek ON, Kurban M (2011) Hourly forecasting of long term electric energy demand using novel mathematical models and neural networks. Data Analytics in Weather Forecasting: A Systematic Review The current data mining software landscape provides some crucial insights into data mining prevalence and adoption across industries: according to analyst predictions, Fuzzy C-means clustering (FCM), known as fuzzy ISODATA, is a clustering algorithm that uses membership degrees to determine the extent to which each data point belongs to a certain cluster. Meteorological Variations of PM2.5/PM10 Concentrations and Particle-Associated Polycyclic Aromatic Hydrocarbons in the Atmospheric Environment of Zonguldak, Turkey. 1, retain the better components, then perform Eq. Statistical analyses, along with forecasting However, real-world optimization problems always involve multiple objectives and so-called multi-objective optimization, which means, in this case, the solutions for a multi-objective problem, which is the main focus of the algorithm, represent the trade-offs between the objectives due to the nature of such problems (Shenfield and Rostami, 2015). doi:10.1016/j.knosys.2018.03.011, Daz-Robles, L. A., Ortega, J. C., Fu, J. S., Reed, G. D., Chow, J. C., Watson, J. G., et al. Proced. In addition, China's environmental supervisors have also issued some plans and programs, including EIA (Environmental Influence Assessment) and Emergency Response for reducing air pollution. This experiment mainly focused on the forecasting performance of each model for PM2.5 of Category II in the first season, with the forecasting results of four different hybrid models (MODEGWO-SVM, MODEGWO-BPNN, MODEGWO-ANFIS, Adam-LSTM) and model selection represented in Table 6 and Figure 4. By parity of reasoning, a similar conclusion can be reached through the analyses of the hourly PM10 forecasting results for Category III (the forecasting results are shown in Table 7 and Figure 5). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). Multi-objective Spotted Hyena Optimizer: A Multi-Objective Optimization Algorithm for Engineering Problems. Pendekatan RapidMiner terhadap deret waktu didasarkan pada dua proses transformasi data utama. AQI is an important evaluation indicator that comprehensively reflects the air pollution status related to human health. In summary, whether for Category III or the other categories (the results are shown in Supplementary Appendix S8 and Supplementary Appendix S9) PM10 forecasting, the model selection system attained the best performance for 13 cities. However, there is no effective rule for establishing the values of these parameters on air pollutants forecasting. Environ. In order to eliminate the difference of the order of magnitude of forecasting metric, the MAE, MAE RMSE, MAPE, STDE, U1, and U2 are normalized. Predicting Weather Forecasting State Based on Data Mining The first dataset includes AQI hourly concentrations collected from January 1, 2017, to December 31, 2018, in BJ-TJ-HE. WebUsing Data Mining for Forecasting Data Management Needs: 10.4018/978-1-59904-951-9.ch124: This chapter illustrates the use of data mining as a computational intelligence In terms of skewness, all data sets are rightward, with values of skewness are greater than 0. 2023 BioMed Central Ltd unless otherwise stated. Both variants are tested with one hour and one minute time step resolution data, the results indicate S2S worked well in both datasets. Step 5. At the same time, the other data sets had a thin tail. (2015). 1) For first season PM2.5 forecasting accuracy, the final forecast results of PM2.5 for six cities in Category II are composed of four hybrid models, which include MODEGWO-SVM, MODEGWO-BPNN, MODEGWO-ANFIS, and Adam-LSTM. The outcome of investigation from these steps will explore the interplay between anomaly detection technique and forecasting model accuracy. 1 There are three stages of data mining Full size image As can be seen The proposed system employed fuzzy C-means cluster algorithm to analyze 13 original AQI series, and fuzzy comprehensive evaluation is used to find out the main air pollutants in each city. TABLE 7. To ensure the forecasting performance, a modified optimization algorithm is used to further optimize the parameters of the best forecasting model (expect LSTM). Webthe overall process of discovering useful knowledge from data and data mining refers to a particular step in the KDD process. (2017). At the end of the forecasting, the WIC value of the testing sample is calculated. From the angle of methodology, various quantitative prediction methods of the atmosphere pollutant concentrations can be classified into two categories, including deterministic models and empirical models (Steffens et al., 2017). Based on the above analysis, it is necessary to overcome these deficiencies and develop a novel and robust air quality warning system. Meteorological and Air Quality Forecasting Using the WRFSTEM Model during the 2008 ARCTAS Field Campaign. 2018) authors implemented a model-based anomaly detection method for very short-term load forecasting. Manag. (PDF) FORECASTING WITH DATA MINING ALGORITHMS In order to reduce the losses caused by air pollution, several health and governmental institutions gather and publish data regarding what is known as AQI to inform people about the state of air pollution. Step 1. Sci. WebThe air quality index (AQI) indicates the short-term air quality situation and changing trend of the city, which includes six air pollutants: PM2.5, PM10, CO, NO2, SO2 and O3. Data mining by example forecasting and cross prediction (2017). There is a possibility that the training accuracy can be very high, and the test accuracy is not high, that is, over-fitting. Energy Informatics Meanwhile, The DA values of the best hybrid model are over 70%, which proves the best models can effectively capture the changing trend of the actual data. 13. In recent years, air pollution has received increasing attention due to the negative effects, such as respiratory diseases, that it has on human health (Jiang et al., 2017). Energ. ( Kitco News) - Cerrado Gold (TSX.V: CERT) reported today that its Minera Don Nicolas gold mine in Argentina produced 13,951 gold equivalent ounces (GEO) in Q1 2023, a 3% improvement year-on-year due to higher recoveries. The final forecasting values are obtained by model selection; based on the results of DEGWO-SVM and DEGWO-BPNN the MAPE values of model selection are 0.65%, 0.75%, 0.97%, and 0.67%. The full contents of the supplement are available online at https://energyinformatics.springeropen.com/articles/supplements/volume-1-supplement-1. A large number of empirical models include statistical models and machine learning models for the forecast of atmospheric pollutant concentrations. Technology Innovation 22, 101441. doi:10.1016/j.eti.2021.101441, Davood, N.-K., Goudarzi, G., Taghizadeh, R., Asumadu-Sakyi, A., and Fehresti-Sani, M. (2021). The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. Since the industrial revolution, many countries have focused on economic development while ignoring air quality, and incidents that cause harm are everywhere. Comparing the four single hybrid models for the hourly NO2 forecasting, the forecasting accuracy of DEGWO-SVM is higher than that of the other three hybrid models. Sort the parent population of gray wolves in a nondecreasing order; The hybrid AQI forecasting system in this paper is composed of the above three parts. In addition, the average MAPE values of model selection for six cities of Category II in four seasons are lower than 1%. Step 2: Feature selection and data setting for each model. In the domain of energy consumption forecasting several techniques are used by researchers which includes traditional methods such as regression, time series, statistical methods along with soft computing techniques such as Artificial Neural Networks (ANNs), Support Vector Machine (SVM), fuzzy logic, and Grey prediction. 14. A New Air Quality Monitoring and Early Warning System: Air Quality Assessment and Air Pollutant Concentration Prediction. Sci. 4) According to the results in Table 6, the three kinds of hybrid models are used to forecast PM10 for four cities of Category III in the fourth season, and the R2 value of each model was greater than 0.99, which shows that these models have a good forecasting performance for the PM10. For example, in the PM2.5 forecasting of Xingtai, the MAPE value of optimal forecasting model (MODEGWO-SVM) is 0.79%, and the MAPE of the model selection is 0.76%, which shows that forecasting accuracy has no significant improvement. The most serious is the well-known London smog event of 1952more than 4,000 deaths in 4days and more than 8,000 deaths in 2months. Specifically, it can not only deeply analyze major pollutants of AQI for BJ-TJ-HE but also approximate the actual values with high accuracy and stability. Application of Improved CFD Modeling for Prediction and Mitigation of Traffic-Related Air Pollution Hotspots in a Realistic Urban Street. To integrate RES in the power grid, forecasting photovoltaic (PV) yield is very important, as the output of PV systems is sensitive to weather conditions and to the varying strength of solar irradiance striking the PV surface throughout the day. Using quarterly U.S. GDP data from 1976 to 2020 we find that the machine 2) Focusing on Category II, it is clear that proposed model based on model selection exhibits the best performance among the single fourth hybrid models implemented for all eight criteria involved. forecast The forecasting approaches which are present in the literature usually utilize proprietary data. Through the use of the AQI it was possible to synthesize, in a single daily value, concentrations of major pollutants in urban areas (NO2, O3, CO, SO2, PM2.5, PM10) for the entire period (Feng et al., 2015). 2014 Int Joint Conf Neural Netw (IJCNN). Hao, Y., Tian, C., and Wu, C. (2019). 2. In (Khatib and Elmenreich 2015), authors proposed a generalized regression artificial neural network for predicting hourly solar radiation. However, complex models such as deep learning models do have a limitation in terms of interpretability. The weather forecasting is the best application in meteorology and it is the most Data mining Research Techniques and scientifically challenging problems in the CFD Modelling of Air Quality in Pamplona City (Spain): Assessment, Stations Spatial Representativeness and Health Impacts Valuation. Trend Analysis of Air Quality Index in Catania from 2010 to 2014. 148, 239257. Create Data Source View. The smaller the g value is, the more support vectors there are, and the greater the smoothing effect will be; the higher accuracy of the training processing cannot be obtained, and the accuracy of the testing processing will also be affected. The term predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance. The most significant point in any kind of air pollution control system is to be able to detect increasing (deterioration) or decreasing (improvement) trends (Hao and Tian, 2018). doi:10.1016/j.egypro.2019.01.952, Pan, L., Sun, B., and Wang, W. (2011). Step 4: Calculate the new U matrix with Eq. The forecasts are used to support flight planning by enabling the representation of important three-dimensional (3-D) atmospheric chemical structures (such as dust storm plumes, polluted air masses originated by large cities, and widespread biomass burning events) and their time evolution, which are often research targets to be detected and investigated through specific flight plans (Latif et al., 2018). Data Mining From Table 4, it can be seen that SVM provides more optimal forecasting value for the three main pollutants at different times, especially in the PM10 forecasting process; the optimal forecasting value for the first quarter and the third quarter is 82.14% (138 optimal forecasting value), and the other four models also provide corresponding optimal forecasting value. The authors compared the accuracy of analytically developed model with three different ANN architectures and achieved highest accuracy with time delay back propagation ANN architecture. ANN has a high ability of self-learning and self-adaptation. 196, 443457. The deterministic model is mainly the chemical transport model (CTM), which is based on the fundamental principles of simulating atmospheric physics and chemistry that involve transportation, emissions, and conversion processes in air pollution (Rivas et al., 2018). The result of model selection for main air pollutants in different seasons. Like weather forecasts, people also long for air quality prediction to arrange their activities and take protective measures in advance (Hao et al., 2021). The BJ-TJ-HE is the national capital region of the People's Republic of China. (2019). Figure 6 shows the forecasting performance with the different configurations of the optimal number of input layers and the number of hidden layers of LSTM and BPNN, in which it is difficult to find a regular correlation between the forecasting performances and the parameters. The process of model selection is as follows: Each model data is divided into 840 training samples, 168 testing samples, and one forecasting value. WebForecasting, data mining, text mining in Excel Uncover new revenue, predict churn or fraud Explore, partition, transform data, extract features Use regression, trees, neural nets, ensembles, PMML Includes both Analytic Solver Desktop and Analytic Solver Cloud Get the 15-Day FREE Trial Watch Video How it works 1 Get the 15-Day FREE Trial As an example, with respect to Tianjin, the DA values of the individual hybrid models are 80.84% (MODEGWO-SVM), 70.06% (MODEGWO-GRNN), and 77.84% (MODEGWO-BPNN), while the DA values of the proposed models is 87.24%, respectively. Environ. A Hybrid Model for PM 2.5 Forecasting Based on Ensemble Empirical Mode Decomposition and a General Regression Neural Network. Predictive Analytics Specifically, the lowest values of MAE are 0.4643, 0.4600, and 0.3869 and of RMSE are 0.7302, 0.7906, and 0.5561, corresponding to PM10 forecasting in Category I in three cities, successively. Notably, Adam-LSTM with complex model structure has longer computing time than the other hybrid models, taking more time in the iterative optimization process. short-term, medium-term and long-term forecasting. Daz-Robles et al. The hybrid algorithm not only improves the global search ability but also effectively avoids the defects of early maturity stagnation and falling into local optimum. The accuracy of model selection depends on the hybrid model, so it is necessary to increase the types of models in the modeling process which ensures that more forecasting results can be obtained, and the optimal forecasting value can be selected in the model selection process. For example, Zhang et al. The selected factors should possess the traits of representativeness, feasibility, and system. Metric MAPE access uniform prediction errors given by (3). According to the implementation of fuzzy comprehensive evaluation results, finding out the main pollutants in each city is another important part of this work. People who work in the data mining field use this type of data analysis to help predict the outcome of business decisions such as moves to increase revenue or reduce risk. The evaluationforecast system developed in this study consists of two parts: evaluation and forecasting. Your privacy choices/Manage cookies we use in the preference centre. The result of fuzzy comprehensive evaluation. Res. Data mining is the application of specific algorithms for extracting patterns from the huge data[23]. Eight performance metrics are applied to assess the performance of the proposed model. In the 1940s, the smog incident in Los Angeles caused many people to have red eyes, pharyngitis, respiratory disease deterioration, and even confusion and pulmonary edema. Data mining assists in the analysis of future patterns and character, enabling companies to make informed decisions.
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