Journal articles: 'Forecasting function'

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Sanders, Nada R. "Managing the forecasting function." Industrial Management & Data Systems 95, no. 4 (May 1995): 12–18. http://dx.doi.org/10.1108/02635579510086689.

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Szewczak, Lara. "Finding Genetic Regulators, Forecasting Function." Cell 174, no. 2 (July 2018): 247–49. http://dx.doi.org/10.1016/j.cell.2018.06.043.

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A . OTHMAN, SAMEERAH, and SHELAN S . ISMAEL. "Forecasting rainfull using transfer function." IRAQI JOURNAL OF STATISTICAL SCIENCES 13, no. 24 (August 28, 2013): 17–44. http://dx.doi.org/10.33899/iqjoss.2013.80692.

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Sen, Rituparna, and Changie Ma. "Forecasting Density Function: Application in Finance." Journal of Mathematical Finance 05, no. 05 (2015): 433–47. http://dx.doi.org/10.4236/jmf.2015.55037.

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Mantica, Giorgio, and B. G. Giraud. "Nonlinear forecasting and iterated function systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 2, no. 2 (April 1992): 225–30. http://dx.doi.org/10.1063/1.165908.

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Sun, Sizhou, Jingqi Fu, Feng Zhu, and Nan Xiong. "A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization." Mathematical Problems in Engineering 2018 (November 14, 2018): 1–21. http://dx.doi.org/10.1155/2018/9287097.

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The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

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Verma, Shilpa, G. T. Thampi, and Madhuri Rao. "ANN based method for improving gold price forecasting accuracy through modified gradient descent methods." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (March 1, 2020): 46. http://dx.doi.org/10.11591/ijai.v9.i1.pp46-57.

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Forecast of prices of financial assets including gold is of considerable importance for planning the economy. For centuries, people have been holding gold for many important reasons such as smoothening inflation fluctuations, protection from an economic crisis, sound investment etc.. Forecasting of gold prices is therefore an ever important exercise undertaken both by individuals and groups. Various local, global, political, psychological and economic factors make such a forecast a complex problem. Data analysts have been increasingly applying Artificial Intelligence (AI) techniques to make such forecasts. In the present work an inter comparison of gold price forecasting in Indian market is first done by employing a few classical Artificial Neural Network (ANN) techniques, namely Gradient Descent Method (GDM), Resilient Backpropagation method (RP), Scaled Conjugate Gradient method (SCG), Levenberg-Marquardt method (LM), Bayesian Regularization method (BR), One Step Secant method (OSS) and BFGS Quasi Newton method (BFG). Improvement in forecasting accuracy is achieved by proposing and developing a few modified GDM algorithms that incorporate different optimization functions by replacing the standard quadratic error function of classical GDM. Various optimization functions investigated in the present work are Mean median error function (MMD), Cauchy error function (CCY), Minkowski error function (MKW), Log cosh error function (LCH) and Negative logarithmic likelihood function (NLG). Modified algorithms incorporating these optimization functions are referred to here by GDM_MMD, GDM_CCY, GDM_KWK, GDM_LCH and GDM_NLG respectively. Gold price forecasting is then done by employing these algorithms and the results are analysed. The results of our study suggest that the forecasting efficiency improves considerably on applying the modified methods proposed by us.

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Levy, William B., Ashlie B. Hocking, and Xiangbao Wu. "Interpreting hippocampal function as recoding and forecasting." Neural Networks 18, no. 9 (November 2005): 1242–64. http://dx.doi.org/10.1016/j.neunet.2005.08.005.

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McGregor. "SNOW AVALANCHE FORECASTING BY DISCRIMINANT FUNCTION ANALYSIS." Weather and Climate 9, no. 2 (1989): 3. http://dx.doi.org/10.2307/44279774.

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Nogales, F. J., and A. J. Conejo. "Electricity price forecasting through transfer function models." Journal of the Operational Research Society 57, no. 4 (April 2006): 350–56. http://dx.doi.org/10.1057/palgrave.jors.2601995.

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Jeong, Yujin, Inchae Park, and Byungun Yoon. "Forecasting technology substitution based on hazard function." Technological Forecasting and Social Change 104 (March 2016): 259–72. http://dx.doi.org/10.1016/j.techfore.2016.01.014.

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Mondal, M. Shahjahan, and Saleh A. Wasimi. "Periodic Transfer Function-Noise Model for Forecasting." Journal of Hydrologic Engineering 10, no. 5 (September 2005): 353–62. http://dx.doi.org/10.1061/(asce)1084-0699(2005)10:5(353).

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Wanto, Anjar, Agus Perdana Windarto, Dedy Hartama, and Iin Parlina. "Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density." IJISTECH (International Journal Of Information System & Technology) 1, no. 1 (November 13, 2017): 43. http://dx.doi.org/10.30645/ijistech.v1i1.6.

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Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

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Hirose, Kei, Keigo Wada, Maiya Hori, and Rin-ichiro Taniguchi. "Event Effects Estimation on Electricity Demand Forecasting." Energies 13, no. 21 (November 10, 2020): 5839. http://dx.doi.org/10.3390/en13215839.

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We consider the problem of short-term electricity demand forecasting in a small-scale area. Electric power usage depends heavily on irregular daily events. Event information must be incorporated into the forecasting model to obtain high forecast accuracy. The electricity fluctuation due to daily events is considered to be a basis function of time period in a regression model. We present several basis functions that extract the characteristics of the event effect. When the basis function cannot be specified, we employ the fused lasso for automatic construction of the basis function. With the fused lasso, some coefficients of neighboring time periods take exactly the same values, leading to stable basis function estimation and enhancement of interpretation. Our proposed method is applied to the electricity demand data of a research facility in Japan. The results show that our proposed model yields better forecast accuracy than a model that omits event information; our proposed method resulted in roughly 12% and 20% improvements in mean absolute percentage error and root mean squared error, respectively.

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El Shafie, Amr H., A. El-Shafie, A. Almukhtar, Mohd R. Taha, Hasan G. El Mazoghi, and A. Shehata. "Radial basis function neural networks for reliably forecasting rainfall." Journal of Water and Climate Change 3, no. 2 (June 1, 2012): 125–38. http://dx.doi.org/10.2166/wcc.2012.017.

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Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data.

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Rahmawati, Nila, and Trianingsih Eni Lestari. "Implementasi Model Fungsi Transfer dan Neural Network untuk Meramalkan Harga Penutupan Saham (Close Price)." Jurnal Matematika 9, no. 1 (June 30, 2019): 11. http://dx.doi.org/10.24843/jmat.2019.v09.i01.p107.

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The multivariate forecasting model is a model of forecasting that takes into the causal relationship between a prediction factor with one or more independent variables. This study uses multivariate forecasting model that are transfer function and neural network model. The transfer function and neural network model are used for forecasting of closing stock price data by considering the opening stock price data as the independent variable in the forecasting model. The data used in this study is the monthly closing stock price and opening stock price data of PT. Bank Central Asia, Tbk. The best model for forecasting of closing stock price is a transfer function model that has MSE, MAPE, and MAE values ??smaller than the neural network model. Keywords: transfer function, neural network, opening stock price, closing stock price

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Gao, Chong, Hai Jie Ma, and Pei Na Gao. "Daily Load Forecasting Based on Combination Forecasting Techniques." Advanced Materials Research 201-203 (February 2011): 2685–89. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2685.

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To improve the accuracy of load forecasting is the focus of the load forecasting. As the daily load by various environmental factors and periodical, this makes the load time series of changes occurring during non-stationary random process. The key of improving the accuracy of artificial neural network training is to select effective training sample. This paper based on the time series forecasting techniques’ random time series autocorrelation function to select the neural network training samples. The method of modeling is more objective. By example, the comparison with autoregressive (AR) Model predictions and BP Artificial Neural Network (ANN) predicted results through error analysis and confirmed the proposed scheme good performance.

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Ahmad, Ahmad. "Transfer Function Models for Forecasting Domestic Water Use." Journal of Social and Development Sciences 6, no. 2 (June 30, 2015): 52–56. http://dx.doi.org/10.22610/jsds.v6i2.842.

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The ability of transfer function models to forecast domestic water use is investigated. Five years monthly time series data on domestic water use, total rainfall and average temperature from Muscat was taken for this study. The transfer function models aim to describe the relationship between input and output systems using a ratio of the polynomials representing the Laplace Transforms of the output, input and the noise in the system. Total rainfall and average temperature were considered as the input series and the domestic water use as an out series. The input series were pre-whiten using Seasonal Autoregressive Integrated Moving Average (SARIMA) models which were identified by Sample Autocorrelation (SAC) and Partial Sample Autocorrelation (PSAC). Four preliminary transfer function models were postulated to describe the output series. The graphs of Sample Cross Correlation (SCC) of water use with rainfall and temperature were made. The final transfer function model was identified by investigating the Residual Sample Cross Correlation (RSSC) which had the form SARIMA(1,1,1)x(1,1,1). This model was then used to generate twelve months out of sample forecasts. The accuracy of forecast error was assessed by mean absolute deviation (MAD), mean square error (MSE) and mean absolute percent error (MAPE). All of these measures had reasonably small values which were 0.105, 0.013 and 1.37% respectively.

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Ho, Ping Chun, and John Z. Yim. "Wave height forecasting by the transfer function model." Ocean Engineering 33, no. 8-9 (June 2006): 1230–48. http://dx.doi.org/10.1016/j.oceaneng.2005.09.003.

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Abdelazeem, S., Ahmed H. Ibrahim, and Hossam E. Hosny. "Probabilistic forecasting of schedule performance using polynomial function." International Journal of Information and Decision Sciences 8, no. 4 (2016): 358. http://dx.doi.org/10.1504/ijids.2016.080454.

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Hosny, Hossam E., Abdelazeem S. Abdelazeem, and Ahmed H. Ibrahim. "Probabilistic forecasting of schedule performance using polynomial function." International Journal of Information and Decision Sciences 8, no. 4 (2016): 358. http://dx.doi.org/10.1504/ijids.2016.10001383.

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Liu, Lon-Mu. "Sales forecasting using multi-equation transfer function models." Journal of Forecasting 6, no. 4 (1987): 223–38. http://dx.doi.org/10.1002/for.3980060402.

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Dargahi-Noubary, G. R. "An envelope function model for forecasting athletics records." Journal of Forecasting 13, no. 1 (January 1994): 11–20. http://dx.doi.org/10.1002/for.3980130103.

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Chang, F. J., Jin-Ming Liang, and Yen-Chang Chen. "Flood forecasting using radial basis function neural networks." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 31, no. 4 (2001): 530–35. http://dx.doi.org/10.1109/5326.983936.

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Steenberg, James W. N., Andrew A. Millward, David J. Nowak, Pamela J. Robinson, and Alexis Ellis. "Forecasting Urban Forest Ecosystem Structure, Function, and Vulnerability." Environmental Management 59, no. 3 (October 24, 2016): 373–92. http://dx.doi.org/10.1007/s00267-016-0782-3.

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Cheremnykh, V. Yu, and L. S. Yakovlev. "Forecasting Activities Reflective Function in a Systemic Crisis." Vestnik Povolzhskogo instituta upravleniya 21, no. 2 (2021): 88–103. http://dx.doi.org/10.22394/1682-2358-2021-2-88-103.

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Issues of the predictive activity methodology regarding changes in self-identification practices of societies are analyzed. A genetic approach to understanding the logic of the development of strategies for developing forecasts is implemented. Types of strategies inherent in traditional, industrial, and post-industrial societies are identified. The conclusion about the necessity of transition to a methodology based on the articulation of dynamic processes, rather than static states, in forecasting activities is substantiated.

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Elliott, Graham, and Allan Timmermann. "Economic Forecasting." Journal of Economic Literature 46, no. 1 (February 1, 2008): 3–56. http://dx.doi.org/10.1257/jel.46.1.3.

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Forecasts guide decisions in all areas of economics and finance and their value can only be understood in relation to, and in the context of, such decisions. We discuss the central role of the loss function in helping determine the forecaster's objectives. Decision theory provides a framework for both the construction and evaluation of forecasts. This framework allows an understanding of the challenges that arise from the explosion in the sheer volume of predictor variables under consideration and the forecaster's ability to entertain an endless array of forecasting models and time-varying specifications, none of which may coincide with the “true” model. We show this along with reviewing methods for comparing the forecasting performance of pairs of models or evaluating the ability of the best of many models to beat a benchmark specification.

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Dawson, C. W., C. Harpham, R. L. Wilby, and Y. Chen. "Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China." Hydrology and Earth System Sciences 6, no. 4 (August 31, 2002): 619–26. http://dx.doi.org/10.5194/hess-6-619-2002.

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Abstract. While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

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Ting, Wang, and Li Xueyong. "Research on short-term electric load forecasting based on extreme learning machine." E3S Web of Conferences 53 (2018): 02009. http://dx.doi.org/10.1051/e3sconf/20185302009.

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As an important support for the development of the national economy, the power industry plays a role in ensuring economic operations. Time series prediction can process dynamic data, is widely used in economics and engineering, and especially is of great practical value in using historical data to predict future development. Under the guidance of extreme learning machine and time series theory, this paper applies the extreme learning machine to the study of time series, and builds a model for load forecasting research. Load forecasting plays an important role in power planning, affecting planning operation modes, power exchange schemes, etc., so load forecasting is very necessary in power planning. First, establish an extreme learning machine model; second, the short-term load forecasting is performed by different activation functions to verify the performance of the activation function.1 After empirical analysis, the activation function with the best predictive ability is obtained.

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Park, Byungkyu, Carroll J. Messer, and Thomas Urbanik. "Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network." Transportation Research Record: Journal of the Transportation Research Board 1651, no. 1 (January 1998): 39–47. http://dx.doi.org/10.3141/1651-06.

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A radial basis function (RBF) neural network has recently been applied to time-series forecasting. The test results of an RBF neural network in forecasting short-term freeway traffic volumes are provided. Real observations of freeway traffic volumes from the San Antonio TransGuide System have been used in these experiments. For comparison of forecasting performances, Taylor series, exponential smoothing method (ESM), double exponential smoothing method, and backpropagation neural network were also designed and tested. The RBF neural network model provided the best performance and required less computational time than BPN. It seems that RBF and ESM can be a viable forecasting routine for advanced traffic management systems. There are some tradeoffs between RBF and ESM. Although the performance of ESM is inferior to RBF, the former does not need a complicated training process or historic database, and vice versa. However, even in the best performance case, 35 percent of the forecast traffic volumes showed 10 percent or more percentage errors. This means that we cannot heavily depend on the forecast traffic volumes as long as we are utilizing the models tested. Further work is needed to provide a more reliable traffic forecasting model.

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Cipollini, Fabrizio, Giampiero M. Gallo, and Alessandro Palandri. "Realized Variance Modeling: Decoupling Forecasting from Estimation*." Journal of Financial Econometrics 18, no. 3 (2020): 532–55. http://dx.doi.org/10.1093/jjfinec/nbaa009.

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Abstract This article evaluates the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and functions delivering estimates (estimation criteria). Our empirical findings highlight that: independently of the econometrician’s forecasting loss (FL) function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the Heterogeneous Autoregressive (HAR) family, for any of the FL functions considered; the (2, 1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-range dependence as does the HAR family.

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Zhao, Zhe, and Xiao Yu Li. "Study of Sequential Minimal Optimization Algorithm Type and Kernel Function Selection for Short-Term Load Forecasting." Applied Mechanics and Materials 329 (June 2013): 472–77. http://dx.doi.org/10.4028/www.scientific.net/amm.329.472.

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Short-term load forecasting is important for power system operation,including preparing plans for generation and supply, arranging the generator to set start or stop, coordinating thermal power units and hydropower units. Support vector machines have advantage in approximating any nonlinear function with arbitrary precision and modeling by studying history data. Based on SVM, this paper selects the sequential minimal optimization (SMO) algorithm to compute load forecasting, because SMO can avoid iterative, so as to short the running time. If we select different kernel functions and the SMO type in the computing process, we will receive different result. Though the analysis of results,the paper obtains the optimal solution in different accuracy or time requirements for short-term load forecasting. By a power plant data, respectively, it discusses from the weekly load forecasts and daily load forecast to play an empirical analysis. It concludes that the selection of ɛ-SVR type and the linear form kernel function is ideal for short-term load forecasting in a not strictly time limits. Otherwise, it will select others in different terms.

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Chang, Wen Yeau. "Wind Energy Conversion System Power Forecasting Using Radial Basis Function Neural Network." Applied Mechanics and Materials 284-287 (January 2013): 1067–71. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1067.

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An accurate forecasting method for wind power generation of the wind energy conversion system (WECS) can help the power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the wind power generation of WECS. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS. The good agreements between the realistic values and forecasting values are obtained; the numerical results show that the proposed forecasting method is accurate and reliable.

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Sun, Sizhou, Lisheng Wei, Jie Xu, and Zhenni Jin. "A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN." Energies 12, no. 3 (January 22, 2019): 334. http://dx.doi.org/10.3390/en12030334.

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Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.

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GUNAWAN, NOVIAN ENDI, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "PERAMALAN JUMLAH PENDERITA DEMAM BERDARAH DENGUE DI KOTA DENPASAR MENGGUNAKAN MODEL FUNGSI TRANSFER MULTIVARIAT." E-Jurnal Matematika 7, no. 1 (February 3, 2018): 64. http://dx.doi.org/10.24843/mtk.2018.v07.i01.p186.

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Forecasting is a way to predict future events. One model in forecasting is a transfer function. The transfer function is a forecasting model that combines characteristics of the ARIMA model with some characteristics of regression analysis. Dengue Hemorrhagic Fever is a major problem in Bali. Recorded Bali Province ranked fourth in the spread of dengue virus and Denpasar City ranked first in the number of death cases of Dengue Hemorrhagic Fever. The purpose of this research is to know the multivariate transfer function model and the prediction of people with Dengue Hemorrhagic Fever in Denpasar City based on the level of rain and humidity. Forecasting results in 2017 in January to June were 46, 51, 226, 625, 1064, 1001, and 580 peoples with a percentage error model transfer function of 17.2%.

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Samimi, Ahmad Jafari. "Forecasting Government Size in Iran Using Artificial Neural Network." Journal of Economics and Behavioral Studies 3, no. 5 (November 15, 2011): 274–78. http://dx.doi.org/10.22610/jebs.v3i5.280.

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In this study, artificial neural network (ANN) for forecasting government size in Iran is applied. The purpose of the study is comparison various architectures, transfer functions and learning algorithms on the operation of network, for this purpose the annual data from 1971-2007 of selected variable are used. Variables are tax income, oil revenue, population, openness, government expenditure, GDP and GDP per capita; these variables are selected based on economic theories. Result shows that networks with various training algorithms and transfer functions have different results. Best architecture is a network with two hidden layer and twelve (12) neuron in hidden layers with hyperbolic tangent transfer function both in hidden and output layers with Quasi -Newton training algorithm. Base on findings in this study suggested in using neural network must be careful in selecting the architecture, transfer function and training algorithms.

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Chang, Wen Yeau. "Power Generation Forecasting of Solar Photovoltaic System Using Radial Basis Function Neural Network." Applied Mechanics and Materials 368-370 (August 2013): 1262–65. http://dx.doi.org/10.4028/www.scientific.net/amm.368-370.1262.

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An accurate forecasting method for power generation of the solar photovoltaic (PV) system can help the power systems operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the power generation of PV system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of power generation of a PV system. The good agreements between the realistic values and forecasting values are obtained; the numerical results show that the proposed forecasting method is accurate and reliable.

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Zhang, Wen Tao, Rui Feng An, and Bin Wu. "The Application of Global Forecast Function in the Power System Load Forecasting Software Development." Applied Mechanics and Materials 313-314 (March 2013): 1347–52. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1347.

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First of all, this paper analyzes the calculation process of the load forecast of power system, and puts forward a new ideas of varieties of load forecasting method according to the classification on this basis, this paper will establish a global predictive function corresponding to different kinds of load forecasting methods, and designed the software on the load forecasting on the basis of such function. This paper describes the programming ideas of using the global predictive function to conduct the single forecast method and combined forecast, which demonstrates its advantages. Finally this paper has introduced the realization of global predictive function in Visual Basic language in programming, and the corresponding interface-building and use method. Actual use shows that, the use of global predictive function can significantly reduce the program size, add flexibility in the load forecasting software, which improves the scalability as well as the extensibility of the program.

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Salpasaranis, Konstantinos, Vasilios Stylianakis, and Stavros Kotsopoulos. "Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries." Advances in Operations Research 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/568478.

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This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers’ population. The method constitutes an expansion of the hybrid Genetic Programming (hGP) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC) and Consumer Prices Index (CPI) leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD) countries shows very satisfactory forecasting performance.

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Zhang, Dai Yuan, and Jian Hui Zhan. "Short-Term Traffic Flow Forecasting of Road Based on Spline Weight Function Neural Networks." Applied Mechanics and Materials 513-517 (February 2014): 695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.695.

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Traditional short-term traffic flow forecasting of road usually based on back propagation neural network, which has a low prediction accuracy and convergence speed. This paper introduces a spline weight function neural networks which has a feature that the weight function can well reflect sample information after training, thus propose a short-term traffic flow forecasting method base on the spline weight function neural network, specify the network learning algorithm, and make a comparative tests bases on the actual data. The result proves that in short-term traffic flow forecasting, the spline weight function neural network is more effective than traditional methods.

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Tran, Kim Thanh, and The Vinh Tran. "THE APPLICATION OF CORRELATION FUNCTION IN FORECASTING STOCHASTIC PROCESSES." Herald of Advanced Information Technology 2, no. 4 (December 4, 2019): 268–77. http://dx.doi.org/10.15276/hait.04.2019.3.

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CHOI, SunWook, TaeShin CHO, WoonHae KIM, and YoungChol KIM. "An Adaptive Storage Function Method for Rainfall-Runoff Forecasting." Transactions of the Society of Instrument and Control Engineers 37, no. 12 (2001): 1156–61. http://dx.doi.org/10.9746/sicetr1965.37.1156.

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., P. Badari Narayana. "WIND ENERGY FORECASTING USING RADIAL BASIS FUNCTION NEURAL NETWORKS." International Journal of Research in Engineering and Technology 04, no. 12 (December 25, 2015): 274–79. http://dx.doi.org/10.15623/ijret.2015.0412054.

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Rusyana, Asep, Nany Salwa, and Ainur Marziah. "Forecasting rainfall using transfer function in Aceh province, Indonesia." Applied Mathematical Sciences 13, no. 7 (2019): 299–307. http://dx.doi.org/10.12988/ams.2019.9121.

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Wong, Chi Heem, and Albert K. Tsui. "Forecasting life expectancy: Evidence from a new survival function." Insurance: Mathematics and Economics 65 (November 2015): 208–26. http://dx.doi.org/10.1016/j.insmatheco.2015.08.006.

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Krol, Robert. "Evaluating state revenue forecasting under a flexible loss function." International Journal of Forecasting 29, no. 2 (April 2013): 282–89. http://dx.doi.org/10.1016/j.ijforecast.2012.11.003.

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AGRAWAL, RANJANA, CHANDRA HAS, and KAUSTAV ADITYA. "Use of discriminant function analysis for forecasting crop yield." MAUSAM 63, no. 3 (January 1, 2022): 455–58. http://dx.doi.org/10.54302/mausam.v63i3.1241.

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The present paper deals with use of discriminant function analysis for developing wheat yield forecast model for Kanpur (India). Discriminant function analysis is a technique of obtaining linear/Quadratic function which discriminates the best among populations and as such, provides qualitative assessment of the probable yield. In this study, quantitative forecasts of yield have been obtained using multiple regression technique taking regressors as weather scores obtained through discriminant function analysis. Time series data of 30 years (1971-2000) have been divided into three categories: congenial, normal and adverse, based on yield distribution. Taking these three groups as three populations, discriminant function analysis has been carried out. Discriminant scores obtained from this have been used as regressors in the modelling. Various strategies of using weekly weather data have been proposed. The models have been used to forecast yield in the subsequent three years 2000-01 to 2002-03 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

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Kari, Tusongjiang, Wensheng Gao, Ayiguzhali Tuluhong, Yilihamu Yaermaimaiti, and Ziwei Zhang. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers." Energies 11, no. 9 (September 14, 2018): 2437. http://dx.doi.org/10.3390/en11092437.

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Forecasting dissolved gas content in power transformers plays a significant role in detecting incipient faults and maintaining the safety of the power system. Though various forecasting models have been developed, there is still room to further improve prediction performance. In this paper, a new forecasting model is proposed by combining mixed kernel function-based support vector regression (MKF-SVR) and genetic algorithm (GA). First, forecasting performance of SVR models constructed with a single kernel are compared, and then Gaussian kernel and polynomial kernel are retained due to better learning and prediction ability. Next, a mixed kernel, which integrates a Gaussian kernel with a polynomial kernel, is used to establish a SVR-based forecasting model. Genetic algorithm (GA) and leave-one-out cross validation are employed to determine the free parameters of MKF-SVR, while mean absolute percentage error (MAPE) and squared correlation coefficient (r2) are applied to assess the quality of the parameters. The proposed model is implemented on a practical dissolved gas dataset and promising results are obtained. Finally, the forecasting performance of the proposed model is compared with three other approaches, including RBFNN, GRNN and GM. The experimental and comparison results demonstrate that the proposed model outperforms other popular models in terms of forecasting accuracy and fitting capability.

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Liu, Xiao Na, Jian Jun Wang, and Teng Fei Zhang. "A Method of Bike Sharing Demand Forecasting." Applied Mechanics and Materials 587-589 (July 2014): 1813–16. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.1813.

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Bike sharing system is an important part of urban public transport system, mainly to undertake the function of connection and transfer with bus system, and connection with private car and satisfy the demand of citizen short-distance travel, and other functions. Setting bike sharing rental point is according to the planning of urban comprehensive transportation, using data on the residents travel , including travel rate, traffic structure, etc. and then to predict the proportion of future bike sharing to the total amount of travel, finally obtain bike sharing overall scale combining Bike sharing turn over rate.

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GURR, TED ROBERT, and MARK IRVING LICHBACH. "Forecasting Internal Conflict." Comparative Political Studies 19, no. 1 (April 1986): 3–38. http://dx.doi.org/10.1177/0010414086019001001.

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This article evaluates a formal theory of domestic political conflict using a forecasting approach. The theory, a mobilization of discontent model, argues that the extent of open political conflict within nations is a function of popular discontent, popular dispositions toward conflict, and the balance of organizational strengt between challengers and the regime. In order to examine the forecasting power of this argument, two competing and less elaborate models of domestic political conflict are also proposed. One, a model of the conflict process, forms linkages between the extent and intensity of conflict. The other is a truly naive model, which represents only a persistence of conflit argument. All three models are used to forecast conflict for 10 randomly selected nations in 1971-1975, and implications for the modelling of domestic political conflict are drawn.

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Journal articles on the topic 'Forecasting function'

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