Support vector regression with chaos-based firefly algorithm for stock market price forecasting

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

Support vector regression with genetic algorithms in forecasting tourism demand

Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

Forecasting systems reliability based on support vector regression with genetic algorithms

Background Leaf area is a crucial indicator of plant growth and physiology, with direct measurements being destructive to the plant. This study aimed to develop and compare machine learning models [support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and deep multilayer perceptron (DMLP)] and linear regression (LRM) for the nondestructive prediction of leaf area in five colored cotton cultivars. A total of 1 334 leaves were sampled, and their length (L), width (W), and leaf area (LA) were determined via digitized images. The models were developed using 70% of the data for training and 30% for validation. Their performance was evaluated using the coefficient of determination ( R 2 ), root mean square error, mean absolute error, mean absolute percentage error, and Willmott's index of agreement. Results The results showed that the machine learning models, notably the ANFIS (triangular membership function), the DMLP (2–16-16–1 configuration), and the SVR [radial basis function (RBF) kernel], significantly outperformed the linear regression models in leaf area estimation accuracy. The ANFIS and DMLP models achieved the highest R 2 (0.979 3, test), followed by the SVR model ( R 2 = 0.979 0, test), all with minimal errors. Among the linear models, the LRM (using the L × W product) was the most effective ( R 2 = 0.978 3). Conclusions On the basis of the performance criteria of the models, the machine learning models are more accurate for the nondestructive estimation of leaf area in colored cotton. The best-performing model (SVR with RBF kernel) was made available in an interactive web application, aiming to optimize crop management with accurate and nondestructive data.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

To improve the prediction accuracy of crude oil price even in current complicated international situation, this paper proposed a novel model linking firefly algorithm (FA) with least squares support vector regression (LSSVR), namely FA-LSSVR. In this hybrid intelligent model, FA is used to find the optimal values of LSSVR parameters (i.e., penalty coefficient and kernel function parameters), in order to achieve fast and accurate prediction results. To evaluate the forecasting ability of FA-LSSVR, its performance is compared with other models, including hybrid intelligent methods (LSSVR models with other popular optimization methods), and single models with given predetermined parameters (i.e., support sector regression (SVR), LSSVR, back-propagation neural network (BPNN), autoregressive integrated moving average (ARIMA)). The empirical results reveal that FA-LSSVR outperforms other benchmarks in terms of prediction accuracy, time saving and robustness, suggesting that the proposed approach is a promising alternative to forecast the crude oil price.

According to an experimental dataset on the tensile strength and elongation of TA15 titanium alloy under different hot deformation process parameters including temperature, strain, strain rate and cooling condition, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, is proposed to establish a model for prediction of the tensile strength and elongation of hot deformed TA15 titanium alloy. For tensile strength, the mean absolute percentage error (MAPE) achieved by SVR is 0.65% and 0.68% for the training and test set, respectively. At the same time, the MAPE for elongation achieved by SVR is 1.51% and 3.36% for the training and test set, respectively. The MAPEs for both tensile strength and elongation achieved by SVR are much smaller than those of BPNN by using identical training and test samples. Accordingly, the established SVR model was adopted to illustrate the relationships among tensile strength, elongation, and the process parameters. From the 3D surface of tensile strength vs. temperature and strain rate, it is found that to reach a higher tensile strength, a strain rate lower than 0.01s-1 is required, and a lower strain will be helpful for achieving the maximum elongation. These suggest that SVR as a novel approach has a theoretical significance and potential practical value in fabrication of TA15 titanium alloy with desired properties.

Predicting dam inflow is critical for human life safety, water resource management, and hydroelectric power generation. While machine learning (ML) models address complex, nonlinear hydrological problems, quantum machine learning (QML) offers greater potential to overcome classical computational limits. This study compares a hybrid quantum neural network (HQNN) with the following two classical models: bidirectional CNN-LSTM and support vector regression (SVR). These models were evaluated to predict monthly inflow to the Mile Mughan Dam, a transboundary hydroelectric and irrigation dam located on the Aras River between Azerbaijan and Iran, using a 14-year dataset (2010–2023) under two scenarios. In total, 70% of data was used for training and 30% for testing. The first scenario encompassed meteorological variables plus three months of inflow lags, and the second included inflow lags only. Model performance was assessed using Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Percentage Error (MAPE), and graphical plots. HQNN showed superior performance across all metrics. In Scenario 1, HQNN achieved R2 = 0.915, RMSE = 37.318 MCM, NSE = 0.908, MAPE = 8.343%; CNN-BiLSTM had R2 = 0.867, RMSE = 46.506 MCM, NSE = 0.858, MAPE = 10.795%; SVR had R2 = 0.846, RMSE = 52.372 MCM, NSE = 0.821, MAPE = 12.772%. In Scenario 2, HQNN maintained strong performance (R2 = 0.855, RMSE = 48.56 MCM, NSE = 0.845, MAPE = 9.979%) and outperformed CNN-BiLSTM (R2 = 0.810, RMSE = 56.126 MCM, NSE = 0.793, MAPE = 11.456%) and SVR (R2 = 0.801, RMSE = 60.336 MCM, NSE = 0.761, MAPE = 12.901%). In Scenario 1 and Scenario 2, HQNN increased the prediction accuracy by 19.76% and 13.47%, respectively, compared to the CNN-BiLSTM model. These results confirm HQNN’s reliability in both multivariate and univariate modeling.

In this study the capability of two advanced hybrid artificial intelligence-based methods is investigated in modelling meteorological droughts based on the standardized precipitation index (SPI) for various time windows. The outcomes are compared with adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA). The best-fitted distribution functions are found to vary with respect to stations and time windows. For Canakkale, Istanbul and Tekirdag stations, the correlation coefficients (CC) of hybridized method of ANFIS with gray wolf optimization (ANFIS-GWO) and ARIMA are in the range of 0.88–0.94, 0.88–0.96 and 0.86–0.94, respectively. The performance index also showed that the ANFIS-GWO provides superior accuracy in modelling droughts, with the minimum value (PI = 0.78) for SPI12 of Canakkale station. Forward-chaining cross-validation and the P value of the Chi-squared test also confirm that ANFIS-GWO is the superior model, in which there is not a significant difference between the trend of the predicted categories and that of the real categories for all stations and SPIs.

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

BackgroundAccurate incidence prediction of sexually transmitted diseases (STDs) is critical for early prevention and better government strategic planning. In this paper, four different forecasting models were presented to predict the incidence of AIDS, gonorrhea, and syphilis.MethodsThe annual percentage changes in the incidence of AIDS, gonorrhea, and syphilis were estimated by using joinpoint regression. The performance of four methods, namely, the autoregressive integrated moving average (ARIMA) model, Elman neural network (ERNN) model, ARIMA-ERNN hybrid model and long short-term memory (LSTM) model, were assessed and compared. For 1-year prediction, the collected data from 2011 to 2020 were used for modeling to predict the incidence in 2021. For 5-year prediction, the collected data from 2011 to 2016 were used for modeling to predict the incidence from 2017 to 2021. The performance was evaluated based on four indices: mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).ResultsThe morbidities of AIDS and syphilis are on the rise, and the morbidity of gonorrhea has declined in recent years. The optimal ARIMA models were determined: ARIMA(2,1,2)(0,1,1)12, ARIMA(1,1,2)(0,1,2)12, and ARIMA(3,1,2)(1,1,2)12 for AIDS, gonorrhea, and syphilis 1-year prediction, respectively; ARIMA (2,1,2)(0,1,1)12, ARIMA(1,1,2)(0,1,2)12, and ARIMA(2,1,1)(0,1,0)12 for AIDS, gonorrhea and syphilis 5-year prediction, respectively. For 1-year prediction, the MAPEs of ARIMA, ERNN, ARIMA-ERNN, and LSTM for AIDS are 23.26, 20.24, 18.34, and 18.63, respectively; For gonorrhea, the MAPEs are 19.44, 18.03, 17.77, and 5.09, respectively; For syphilis, the MAPEs are 9.80, 9.55, 8.67, and 5.79, respectively. For 5-year prediction, the MAPEs of ARIMA, ERNN, ARIMA-ERNN, and LSTM for AIDS are 12.86, 23.54, 14.74, and 25.43, respectively; For gonorrhea, the MAPEs are 17.07, 17.95, 16.46, and 15.13, respectively; For syphilis, the MAPEs are 21.88, 24.00, 20.18 and 11.20, respectively. In general, the performance ranking of the four models from high to low is LSTM, ARIMA-ERNN, ERNN, and ARIMA.ConclusionThe time series predictive models show their powerful performance in forecasting STDs incidence and can be applied by relevant authorities in the prevention and control of STDs.

The blast furnace is a master iron-producing plant of iron and steel factories and affected by several process parameters as well as top gas pressure , which is a key process control phenomenon to maintain stability and operational productivity in such plants. Blast furnace operation is not tolerant to any interruption, unbalanced operations, momentary disturbances or loss of control due to its nature of intensive chemical reactions and heat balance requirements. Consequently, it is crucial to monitor and control top gas system components of the furnace with instrumentation measurements to maintain stable, efficient operation and system safety ongoing. In this study, a novel top gas pressure tracking system is developed using the chronologically obtained live process data of Erdemir BF#2 in Turkey. Eight process parameters are considered as input parameters as per the plant maintenance team's recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. Performance and success ratio analysis is carried out using coefficient of determination ( R2), mean absolute percentage error and root mean squared error terms. The best performing model output for the adaptive neuro fuzzy inference system is found to be 0.95, 1.21 and 0.023, and slightly lower performance is obtained for the artificial neural network model with the output values of 0.94, 0.029 and 1.32 against R2, mean absolute percentage error and root mean squared error terms, respectively. The maximum prediction error is found to be 9.85% and 10.2%, and the average prediction error is found to be 1.19% and 1.29% for adaptive neuro fuzzy inference system and ANN models, respectively, for optimum simulations. The proposed neuro-fuzzy-driven top gas pressure prediction system is unique in the literature and should be integrated into existing control systems to improve operational awareness and sustainability or can be used as input guidance for a possible future top gas recovery system.

Forecasting studies are extremely important in the technical, social and economic research. Generally, we know it is very difficult to forecast with higher accurate about a system by using recent values. In the scientific literature, the forecasting studies of energy, personnel planning, production planning, climate changes, sales and marketing and economics etc. are frequently found. In this paper, for an emergency calls center in Isparta province of Turkey an artificial neural network (ANN) forecasting model was developed to determine the number of calls for as health, fire and security services on a pilot implementation of the emergency calls center on a single number 112. In the developed model, the gradient descent with adaptive learning and momentum (GDX) algorithm is selected as the training algorithm with feed-forward back-propagation by using 80% of input data and the 20% of input data is used for testing set data from last month. After the testing, the mean absolute percentage error (MAPE) rate is obtained as 4.5% and it is useful to test. In addition, the forecasting results of the next month are shown that the MAPE values are 2.65%, 6.40% and 5.24% with ANN, trend analysis and ARIMA (1 1 1) models respectively and, the number of calls are found separately on the types of calls in daily. Consequently, the developed model by using ANN to forecast the number of calls in an emergency call center is more accurate than the trend analysis and ARIMA models.

Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for analyzing tomato composition. This study proposes a digital method to predict four minerals (calcium, potassium, phosphorus, and magnesium) in beef-type tomato using machine learning models, including k-nearest neighbors (kNN), artificial neural networks (ANNs), and Support Vector Regression (SVR). The models were discriminated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The kNN model showed the best performance for estimation of quantity of calcium, potassium, phosphorus, and magnesium. The results demonstrate that kNN consistently outperforms ANNs and SVR across all target nutrients, achieving the highest R2 and the lowest error metrics (RMSE, MAE, and MAPE). Notably, kNN achieved an exceptional R2 of 0.8723 and a remarkably low MAPE of 3.95% in predicting phosphorus. This study highlights how machine learning can provide a versatile, accurate, and efficient solution for tomato mineral analysis in digital agriculture.