forecasting in data miningforecasting in data mining

TABLE 6. Therefore, the parameters of the neural network are set by means of simulation experiment and optimization algorithm: the experimental design is as follows: This processing configured various input layers and a number of hidden layers to find out the influence of the usage of recent data on the performance of different ANN models. In terms of skewness, all data sets are rightward, with values of skewness are greater than 0. doi:10.1016/j.wasman.2017.03.044, Zhang, L., Lin, J., Qiu, R., Hu, X., Zhang, H., Chen, Q., 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. past power data. Gayen, S., and Biswas, A., (2021). Current Trends & Future Scope of Data Mining | Datamation For example, you might want to predict the amount of expected downtime for a 2 to generate. A good overview of these techniques can be found in (Suganthi and Samuel 2012). Hence, they are usually considered as an important parameter in training the prediction algorithm. Int J Photoenergy 14:110, Saleh AI, Rabie AH, Abo-Al-Ez KM (2016) A data mining based load forecasting strategy for smart electrical grids. In (Marino et al. 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. Flowchart of forecasting process based on predictive data mining techniques. These performance metrics are defined in Table 3. 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). Mater. If it is less than a certain threshold, or if the amount of change from the value of the last value function is less than a certain threshold, the algorithm stops. 2016), as both have a direct impact on the forecasting model performance. 2015) provides a global error measure throughout the entire forecasting period, given by (2), 3. Step 4: Calculate the distance between other gray wolf individuals in the population and the optimal X, X, and X according to Eqs 35. Implicitly it determines the distribution of data after mapping to a new feature space. Support Vector Machines for Classification and Regression. Meteorological Variations of PM2.5/PM10 Concentrations and Particle-Associated Polycyclic Aromatic Hydrocarbons in the Atmospheric Environment of Zonguldak, Turkey. 196, 443457. The process of fuzzification constitutes the process of membership calculation by using MFs. Air pollution has become the fourth leading health risk factor for China after smoking, diet, and obesity (Zhang et al., 2018). The set of factors for the evaluation object is determined. Sol Energy 111:157175. Trend Analysis of Air Quality Index in Catania from 2010 to 2014. These limitations lead to several interesting characteristics of energy forecasting, which includes data collection and the need for precise accuracy. The design experiment, data analysis, and paper writing were conducted by YH; the forecasting experiment and data analysis were completed by YH and CW; supervision, paper writing, and editing were conducted by CW and YD; validation, methodology, paper editing, and supervision were handled by QL and GZ. Sustainable Cities Soc. Opposition-based Multi-Objective Whale Optimization Algorithm with Global Grid Ranking. Environ. forecast In the forecasting process, an improved multi-objective optimization algorithm is used to optimize the parameters of the single forecasting model, which not only improves the prediction accuracy but also improves the stability of the single model. The full contents of the supplement are available online at https://energyinformatics.springeropen.com/articles/supplements/volume-1-supplement-1. In summary, for the Category I and Category III PM2.5 forecasting list in Supplementary Appendices S5S7, the model selection forecasting system exhibits the best forecasting accuracy among the different hybrid models for four seasons. Urbancok, D., Payne, A., and Webster, R. (2017). In this paper we aim to assess the performance of a forecasting model which is a weather-free model created using a database containing relevant information about past produced power data and data mining techniques. WebThis paper presents data mining based solution for demand forecasting and product allocations applications. A comparative online sales forecasting analysis: Data mining The data mining technique is used for forecasting and improves the performance. Correspondence to Air Pollution: A Review and Analysis Using Fuzzy Techniques in Indian Scenario. Definition of the performance metrics. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Cite this article. Update the position using the following equation; Obtain a mutant population of gray wolves using the following equation; F is the scaling factor, g=0, 1, 2,, MaxGen, MaxGen is the maximum number of the iteration. Neurocomputing 5. doi:10.1016/j.neucom.2019.02.054, Li, Z., Fung, J. C. H., and Lau, A. K. H. (2018). On the contrary, DEGWO-ANFIS has the lowest effectiveness. A Multi Objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification. 1. In the past decades, different approaches for forecasting energy production, distribution and consumption had been implemented. The evaluation rank standard is determined. Estimating agricultural production is a difficult task for our country, particularly given the current population situation. A Bayesian Hierarchical Model for Urban Air Quality Prediction under Uncertainty. 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. We first split the data into training and testing datasets and then run the machine learning algorithm on the training dataset to generate the prediction model. Predictive big data analytics for supply chain demand In this work we plan to include three steps: In the first step, data pre-processing techniques are applied to perform anomaly detection and outlier rejection. A flow chart of the hybrid model is presented in Figure 1. Storage of electrical energy is necessary in the case when there is excess power production from the RES and less load demand. A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in Beijing, China. Proced. 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. Then we use the test dataset to evaluate the model. (2018). Ecol. The Art and Science of Machine Intelligence, 77105. A Novel Hybrid Bat Algorithm for Solving Continuous Optimization Problems. doi:10.1007/978-3-030-18496-4_3, Lanzafame, R., Monforte, P., Patan, G., and Strano, S. (2015). Res. AQI is an important evaluation indicator that comprehensively reflects the air pollution status related to human health. *Correspondence: Yuanchang Deng, dengych@mail.sysu.edu.cn; Chen Wang, wangch339@mail.sysu.edu.cn, Artificial Intelligence-Based Forecasting and Analytic Techniques for Environment and Economics Management, View all A report issued by the World Health Organization (WHO) acknowledges that air pollution is one of the biggest health risks (Xu et al., 2016). Cerrado Gold reports net loss of $7.4M in Q1, expects - KITCO In this subsection, the relative methods are presented in detail, including the data mining technique, forecasting model, and the DEGWO) algorithm. J. Environ. doi:10.1016/j.envsoft.2019.02.017, Brereton, R. G., and Lloyd, G. R. (2010). Hao, Y., Tian, C., and Wu, C. (2019). Some of the recent work on anomaly detection is presented in (Table1). Data mining combines statistics, artificial intelligence and machine learning to find patterns, relationships and anomalies in large data Cybern. Syst. 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. In summary, with the rapid development of ANN, it has become a powerful tool to solve prediction problems. A large sample of the times series is another reason that the training stability of the neural network can be ensured. This can be done using a variety of methods, including regression analysis, In addition, air pollution in China is also quite serious. 13. Data mining is the application of specific algorithms for extracting patterns from the huge data[23]. CSEE J Power Energy Syst 1:3846, Zhang J, Florita A, Hodge B, Lu S, Hamann HF, Banunarayan V, Brockway AM (2015) A suite of metrics for assessing the performance of solar power forecasting. Then, the position of the gray wolf individual is updated by the intersection and selection operations of DE, and the iterative update is repeated until the optimal one is selected. The process of establishing a fuzzy synthetic evaluation (FSE) system is as follows (Lu et al., 2011). Obtain a child population of gray wolves using the following equation; for each individual Parenti in a parent population of gray wolves. Step 3: Calculate the value function according to Eq. TABLE 3. If the WIC of the ith model is the smallest, the forecasting value of the ith model provides the optimal forecasting value. 158, 29222927. FIGURE 6. Res. TABLE 4. 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. WebThe primary benefit of data mining is its power to identify patterns and relationships in large volumes of data from multiple sources. data mining 12, 4656. Near-port Air Quality Assessment Utilizing a mobile Measurement Approach. doi:10.1016/j.envsci.2020.10.004, Hao, Y., Niu, X., and Wang, J. Moreover, this paper establishes multiple hybrid models and uses the model selection method to find the best forecasting value, in which the final forecasting accuracy is improved but needs more computing time. 18XTJ003). Front. Data Mining Using quarterly U.S. GDP data from 1976 to 2020 we find that the machine Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city. The authors of (Saleh et al. By continuing you agree to the use of cookies. In this study, a model selection forecasting system is proposed that consists of data mining, data analysis, model selection, and multi-objective optimized modules and effectively solves the problems of air pollutants monitoring. (2017). Step 6: Update the positions of the top three gray wolf individuals X, X, and X. Anfis: Adaptive-Network-Based Fuzzy Inference System. The result of fuzzy comprehensive evaluation is shown in Table 1, which found that the main air pollutants are PM10, PM2, and NO2 in 13 cities. Cookies policy. In energy and power applications, anomaly detection emerges as an important aspect in fields like electric load forecasting (Chen et al. Weather Prediction Using Data Mining Atmos. doi:10.1016/j.scitotenv.2018.08.315, Shenfield, A., and Rostami, S. (2015). A New Air Quality Monitoring and Early Warning System: Air Quality Assessment and Air Pollutant Concentration Prediction. Soft Comput. Based on the above analysis, it can be seen that none of the models has been playing the best forecasting performance in the forecasting process, and various hybrid models are needed to make up for the shortcomings of the single hybrid model. FIGURE 1. Predicting Weather Forecasting State Based on Data Mining 115, 2634. Gl, Y. S., Dabanl, ., iman, E., and en, Z. 3) In the past, many air quality studies focused on eliminating the effects of noise on data processing and less on the feature extraction of data. Two variants of LSTM are presented, standard LSTM and the LSTM-based Sequence-to-Sequence (S2S) architecture. 9:761287. doi: 10.3389/fenvs.2021.761287. What Is Data Mining? How It Works, Techniques & Examples Next, we use long short-term memory (LSTM), backpropagation neural network (BPNN), adaptive network-based fuzzy inference system (ANFIS), generalized regression neural network (GRNN), and SVM models to forecast the main air pollutants time series, and a developed new metric is used to select optimal forecasting model. Authors in (Gandelli et al. It is noteworthy that these values do represent real measured value and in some circumstances extreme values may indicate sudden events. FIGURE 2. Heliyon 4, 133. Atmos. Building Environ. Decision Tree Based on the above analysis, it is necessary to overcome these deficiencies and develop a novel and robust air quality warning system. xpk(up) is the upper bound of the pth component of the kth individual. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. J. Environ. In the Data Project Explorer, expand the project To avoid underfitting and overfitting cross validation will be performed. In (Alanazi et al. A Hybrid Model for PM 2.5 Forecasting Based on Ensemble Empirical Mode Decomposition and a General Regression Neural Network. Creative Commons Attribution License (CC BY). Eight performance metrics are applied to assess the performance of the proposed model. Output: Descriptive data mining produces summaries and visualizations of the data. For example, Zhang et al. 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 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. The average value of NO2 in the different cities is between 22.2525 and 49.4348g/m3, in which the average value in Xingtai is higher than in the other cities. Although the model selection can improve the forecasting accuracy, in the PM2.5 forecasting of some cities, due to the better forecasting performance of the single hybrid model, the forecasting accuracy of the model selection is not significantly improved. The maximum reduction of MAPE for the proposed model compared with the other hybrid models is approximately 71.18% in Beijing's NO2 forecasting, 53.93% in Baoding's NO2 forecasting, and 61.61% in Langfang's NO2 forecasting, respectively. 45. doi:10.1016/j.atmosenv.2011.02.073, Dass, A., Srivastava, S., and Chaudhary, G. (2021). J. Environ. This section mainly discusses the hyperparameter related to the SVM and ANN model that would influence the forecasting performance. Most of the MAPEs are between 6% and 7%. 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 Data is at the center of decision-making across departments and roles in the dynamic market. Air Quality (AQ) Identification by Innovative Trend Diagram and AQ index Combinations in Istanbul Megacity. For the unknown samples, the classification effect is very poor. We use cookies to help provide and enhance our service and tailor content and ads. Weather Research and Forecasting Model: Weather forecasting is an important area of analysis in life also future is huge essential attributes to forecast for agriculture sectors. Environ. (2011). Both variants are tested with one hour and one minute time step resolution data, the results indicate S2S worked well in both datasets. The idea is to choose an appropriate anomaly detection technique and data-driven methodology for energy production forecasting along with developing a unified model for long-term forecasting with step of short-term (hourly) accuracy. Manag. 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. Energy forecasting algorithms are trained and tested on energy consumption and production datasets. Analysis and Forecasting of the Particulate Matter (PM) Concentration Levels over Four Major Cities of China Using Hybrid Models. Data Mining and Data Forecasting | IMSL Atmos. Int J Photoenergy. Atmos. CFD Modelling of Air Quality in Pamplona City (Spain): Assessment, Stations Spatial Representativeness and Health Impacts Valuation. The datasets of hourly concentrations of the six major air pollutants used in this study are retrieved from the website of the China National Environment Monitoring Centre (http://www.cnemc.cn/sssj/). doi:10.1016/j.jhazmat.2009.05.029, Bessagnet, B., Couvidat, F., and Lemaire, V. (2019). In addition, all the R2 values of the proposed model are over 90%, which underlines the higher fitting effect on Category II. IECON 2016-42nd Ann Conf IEEE Ind Electron Soc:70467051. The forecasting approaches which are present in the literature usually utilize proprietary data. The accuracy of the testing sample is calculated by using the WIC. Crop yield forecasting using data mining Pallavi Kamath , Patil , Shrilatha S , Sushma , Sowmya Add to Mendeley https://doi.org/10.1016/j.gltp.2021.08.008 Get rights TABLE 9. Environ. Any model has its inevitable shortcomings, and due to the advent of the world's big data era, data mining techniques such as decomposition methods (Gl et al., 2019), feature selection techniques (Pan et al., 2011), and optimization algorithms (Liu et al., 2019) combined with artificial intelligence technology are more operational. Common statistical models for air quality prediction include autoregressive (AR) models, moving average (MA) models, autoregressive integrated moving average (ARIMA) models, and multiple linear regression (MLR) models. Energy Informatics Subsequently, the marching process of our developed combined model is demonstrated. In this study, we used the trapezoidal membership to calculate the membership value. Data mining is the process of analyzing large amounts of data in order to identify patterns, anomalies and correlations. The results indicated higher accuracy when using FFBP. A time series is simply a series of data points ordered in time. 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. Although there are many studies on the tuning of the parameters of the neural network, it is obvious that the selection of the whole parameter space is beyond the scope of this study. Sci. :19571962, Khatib T, Elmenreich W (2015) A model for hourly solar radiation data generation from daily solar radiation data using a generalized regression artificial neural network. doi:10.1016/j.apr.2017.04.003. Eng. doi:10.1016/j.atmosenv.2016.10.046, Yang, Z., and Wang, J. SVM has two very important parameters: c and g. c is the penalty coefficient, that is, tolerance of errors. 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). 3) The forecasting metric of the single hybrid models and the proposed model in Table 4 indicates that the proposed model based on model selection performs better than the single hybrid model in Category III. In 1930, the Mas Valley event in Belgium caused nearly 60 deaths in a week. 1, retain the better components, then perform Eq. To perform predictions typically larger datasets in connection with deep learning are becoming common. 113, Dobschinski J, Bessa R, Du P, Gleiser K, Haupt SE, Lange M, Mhrlen C, Nakafuji D, Rodriguez M (2017) Uncertainty forecasting in a nutshell: prediction models designed to prevent significant errors. These datasets contain energy readings from the smart meter and power output produced by PV. 2 to select new individuals and calculate the objective function values of all gray wolf individuals. 4) The model selection index is used to select the optimal forecasting value from the optimal hybrid model. 136, Ramsami P, Oree V (2015) A hybrid method for forecasting the energy output of photovoltaic systems. 2014; Chakhchoukh et al. Data Mining (2019). (PDF) FORECASTING WITH DATA MINING ALGORITHMS In (Luo et al. Manage. rand(0, 1) represents a random number in [0, 1]. Pol. 42, 83318340. 2014 Int Joint Conf Neural Netw (IJCNN). Copyright 2023 Elsevier B.V. or its licensors or contributors. ES analysed related work, identified open issues, and developed a research proposal related to her PhD project. 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. 244, 118556. A Novel Hybrid Strategy for PM 2.5 Concentration Analysis and Prediction. Innovative computing. The simulation result of each ANN model. Energy forecasting based on predictive data mining techniques in smart energy grids, $$ \rho =\frac{\mathit{\operatorname{cov}}\left(\rho, \overline{\rho}\right)}{\sigma_{\rho }{\sigma}_{\overline{\rho}}} $$, $$ \mathrm{RMSE}=\sqrt{\frac{1}{N}{\sum}_{i=1}^N{\left({p}_{pred}-{p}_{meas}\right)}^2} $$, $$ \mathrm{MAPE}=\frac{100}{N}{\sum}_{i=1}^N\left|\frac{p_{pred}-{p}_{meas}}{p_0}\right| $$, https://doi.org/10.1186/s42162-018-0048-9, Proceedings of the 7th DACH+ Conference on Energy Informatics, https://energyinformatics.springeropen.com/articles/supplements/volume-1-supplement-1, http://creativecommons.org/licenses/by/4.0/. Statistic of each main air pollutant in different cities. 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%. Since 2013, China has also begun to evaluate the quality of air through AQI values and graded the city's air quality by AQI values. Compared with the optimal hybrid model, the model selection is approximately reduced by 10%. 116, 100109. The input variables and prediction horizon affect the accuracy of the prediction model. Authors in (Dolara et al. Eight evaluation criteria are applied to estimate the forecasting performance, namely, mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Theil U statistic 1 (U1), and Theil U statistic 2 (U2) were calculated for all the fits; the goodness of forecasting fit (R2) and the standard of forecasting error (STDE) indicates the stability of the forecasting models; and the direction accuracy (DA) evaluates the optimal decision-making, often relying on correct forecasting directions or turning points between the actual and forecasting values. The Long-Term Assessment of Air Quality on an Island in Malaysia. 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. Difference Between Descriptive and Predictive Data Mining The main contributions of this paper are as follows: 1) The fuzzy comprehensive evaluation is established for six air pollutants, which calculates the fuzzy membership degree of each pollutant and determines the main pollutants of each city. In general, the relevant variables which are available as inputs of the prediction model of solar power includes historical measurements of PV generation, historical measurements of explanatory variables like temperature, global irradiance, wind speed or cloud coverage (Wan et al. In 1973, Bezdek proposed the algorithm as an improvement to the early hard C-means clustering (HCM) method (Gayen and Biswas, 2021). In large datasets, there is often the case where we have many ineffective features and a feature selection process could minimize the considered features to effective ones.