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Journal articles on the topic 'Forecasting accuracy'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 21, 2016): 813. http://dx.doi.org/10.17722/ijme.v7i2.262.

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Chindia, E. W. "Forecasting Techniques and Accuracy of Performance Forecasting." International Journal of Management Excellence 7, no. 2 (August 31, 2016): 813–20. http://dx.doi.org/10.17722/ijme.v7i2.851.

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This article explores the impact of the different forecasting methods (FMs) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs), in Kenya. The objective of the study was to assess if the different forecasting methods have an influence on any of the aspects of measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, LMFs tend to hire skilled forecasters, to a great extent, to ensure APF when preparing future budgets. The different types of forecasting techniques have been known to influence the behavior of operations resulting in the formulation of either accurate or inaccurate forecasts resulting in either adverse or favorable organizational performance. The study used the three known forecasting methods, objective, subjective and combined forecasting techniques against measures of APF, expected value, growth in market share, return on assets and return on sales. Regression analysis was used applying data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that APF is influenced by each of the forecasting methods in different ways.

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Doh, Joon-Chien. "Accuracy of Expenditure Forecasting." Asian Journal of Public Administration 11, no. 2 (December 1989): 200–215. http://dx.doi.org/10.1080/02598272.1989.10800221.

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Kimes, S. E. "Group Forecasting Accuracy in Hotels." Journal of the Operational Research Society 50, no. 11 (November 1999): 1104. http://dx.doi.org/10.2307/3010081.

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Kimes, S. E. "Group forecasting accuracy in hotels." Journal of the Operational Research Society 50, no. 11 (November 1999): 1104–10. http://dx.doi.org/10.1057/palgrave.jors.2600770.

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6

Makridakis, Spyros. "Forecasting accuracy and system complexity." RAIRO - Operations Research 29, no. 3 (1995): 259–83. http://dx.doi.org/10.1051/ro/1995290302591.

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7

Köchling, Gerrit, Philipp Schmidtke, and Peter N. Posch. "Volatility forecasting accuracy for Bitcoin." Economics Letters 191 (June 2020): 108836. http://dx.doi.org/10.1016/j.econlet.2019.108836.

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Chindia, E. W. "Forecasting Techniques, External Operating Environment and Accuracy of Performance Forecasting." Advances in Economics and Business 4, no. 8 (August 2016): 468–75. http://dx.doi.org/10.13189/aeb.2016.040809.

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Liu, Dunnan, Yu Hu, Yujie Xu, and Canbing Li. "“Section to Point” Correction Method for Wind Power Forecasting Based on Cloud Theory." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/897952.

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As an intermittent energy, wind power has the characteristics of randomness and uncontrollability. It is of great significance to improve the accuracy of wind power forecasting. Currently, most models for wind power forecasting are based on wind speed forecasting. However, it is stuck in a dilemma called “garbage in, garbage out,” which means it is difficult to improve the forecasting accuracy without improving the accuracy of input data such as the wind speed. In this paper, a new model based on cloud theory is proposed. It establishes a more accurate relational model between the wind power and wind speed, which has lots of catastrophe points. Then, combined with the trend during adjacent time and the laws of historical data, the forecasting value will be corrected by the theory of “section to point” correction. It significantly improves the stability of forecasting accuracy and reduces significant forecasting errors at some particular points. At last, by analyzing the data of generation power and historical wind speed in Inner Mongolia, China, it is proved that the proposed method can effectively improve the accuracy of wind speed forecasting.

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10

Geurts, Michael D., and H. Dennis Tolley. "Causal partitioning and sales forecasting accuracy." Journal of Statistical Computation and Simulation 37, no. 1-2 (October 1990): 1–12. http://dx.doi.org/10.1080/00949659008811289.

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11

Pitsyk, V. V. "Accuracy Forecasting in System Tolerance Monitoring." Measurement Techniques 47, no. 7 (July 2004): 643–48. http://dx.doi.org/10.1023/b:mete.0000043650.20162.9b.

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12

Wozniak, P. P., B. M. Konopka, J. Xu, G. Vriend, and M. Kotulska. "Forecasting residue–residue contact prediction accuracy." Bioinformatics 33, no. 21 (June 26, 2017): 3405–14. http://dx.doi.org/10.1093/bioinformatics/btx416.

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Hassani, Hossein, Emmanuel Sirimal Silva, Nikolaos Antonakakis, George Filis, and Rangan Gupta. "Forecasting accuracy evaluation of tourist arrivals." Annals of Tourism Research 63 (March 2017): 112–27. http://dx.doi.org/10.1016/j.annals.2017.01.008.

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Lee, Cheng-Few, K. Thomas Liaw, and Chunchi Wu. "Forecasting accuracy of alternative dividend models." International Review of Economics & Finance 1, no. 3 (January 1992): 261–70. http://dx.doi.org/10.1016/1059-0560(92)90015-5.

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15

Hitam, Nor Azizah, and Amelia Ritahani Ismail. "Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 1121. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp1121-1128.

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Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

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Hutton, Amy P., and Phillip C. Stocken. "Prior Forecasting Accuracy and Investor Reaction to Management Earnings Forecasts." Journal of Financial Reporting 6, no. 1 (March 1, 2021): 87–107. http://dx.doi.org/10.2308/jfr-2020-005.

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ABSTRACT We examine the properties of firms' forecasting records and whether the accuracy of their prior earnings forecasts affects investor response to their subsequent forecasts.Within the context of a Bayesian model of investor learning, we find that the stock price response to management forecast news is increasing in prior forecast accuracy and also in the length of a firm's forecasting record. Further, we document that investors are more responsive to extreme good and bad news forecasts when a firm has an established forecasting record. Overall, these results suggest that a firm's prior forecasting behavior allows it to establish a forecasting reputation, and that market forces encourage accurate forecasting as firms benefit from having a reputation for forecasting accurately. Data Availability: Data are available from public sources cited in the text. JEL Classifications: G19; G39; D89; M40.

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Hezel, Dianne M., S. Evelyn Stewart, Bradley C. Riemann, and Richard J. McNally. "Affective forecasting accuracy in obsessive compulsive disorder." Behavioural and Cognitive Psychotherapy 47, no. 5 (March 27, 2019): 573–84. http://dx.doi.org/10.1017/s1352465819000134.

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AbstractBackground:Research indicates that people suffering from obsessive compulsive disorder (OCD) possess several cognitive biases, including a tendency to over-estimate threat and avoid risk. Studies have suggested that people with OCD not only over-estimate the severity of negative events, but also under-estimate their ability to cope with such occurrences. What is less clear is if they also miscalculate the extent to which they will be emotionally impacted by a given experience.Aims:The aim of the current study was twofold. First, we examined if people with OCD are especially poor at predicting their emotional responses to future events (i.e. affective forecasting). Second, we analysed the relationship between affective forecasting accuracy and risk assessment across a broad domain of behaviours.Method:Forty-one OCD, 42 non-anxious, and 40 socially anxious subjects completed an affective forecasting task and a self-report measure of risk-taking.Results:Findings revealed that affective forecasting accuracy did not differ among the groups. In addition, there was little evidence that affective forecasting errors are related to how people assess risk in a variety of situations.Conclusions:The results of our study suggest that affective forecasting is unlikely to contribute to the phenomenology of OCD or social anxiety disorder. However, that people over-estimate the hedonic impact of negative events might have interesting implications for the treatment of OCD and other disorders treated with exposure therapy.

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Šečkute, Laima, and Arnoldina Pabedinskaite. "APPLICATION OF FORECASTING METHODS IN BUSINESS." Journal of Business Economics and Management 4, no. 2 (June 30, 2003): 144–57. http://dx.doi.org/10.3846/16111699.2003.9636048.

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In order to provide Lithuanian enterprises with methodical recommendations regarding forecasting, the authors of this article have analysed the advantages and limitations of various forecasting methods. The problem of the accuracy of business forecasts is considered in greater detail, and the practical ways of doing the evaluation and assurance of the accuracy of the forecasting methods under discussion are presented here. The authors of the article suggest that an integrated forecasting model should be applied as it is much more accurate than any forecasting that is provided by the mentioned methods when taken separately. This will help to reduce the cost of forecasting, which is of utmost importance to Lithuanian businesses.

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19

Wang, Yinao. "The grey interval prediction method and its prediction accuracy." Grey Systems: Theory and Application 4, no. 2 (July 29, 2014): 339–46. http://dx.doi.org/10.1108/gs-05-2014-0013.

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Purpose – The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are construction, the general rule which must satisfy is studied, grey wrapping band forecasting method is perfect. Design/methodology/approach – A forecasting method puts forward a process of prediction interval. It also elaborates on the meaning of interval (the probability of the prediction interval including the real value of predicted variable). The general rule is abstracted and summarized by many forecasting cases. The general rule is discussed by axiomatic method. Findings – The prediction interval is categorized into three types. Three axioms that construction predicted interval must satisfy are put forward. Grey wrapping band forecasting method is improved based on the proposed axioms. Practical implications – Take the Shanghai composite index as the example, according to the K-line diagram from 4 January 2013 to 9 May 2013, the reliability of predicted rebound height of subsequent two or three trading day does not exceed the upper wrapping curve is 80 per cent. It is significant to understand the forecasting range correctly, build a reasonable range forecasting method and to apply grey wrapping band forecasting method correctly. Originality/value – Grey wrapping band forecasting method is improved based on the proposed axioms.

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20

Banihabib, Mohammad Ebrahim, Arezoo Ahmadian, and Mohammad Valipour. "Hybrid MARMA-NARX model for flow forecasting based on the large-scale climate signals, sea-surface temperatures, and rainfall." Hydrology Research 49, no. 6 (June 20, 2018): 1788–803. http://dx.doi.org/10.2166/nh.2018.145.

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Abstract In this study, to reflect the effect of large-scale climate signals on runoff, these indices are accompanied with rainfall (the most effective local factor in runoff) as the inputs of the hybrid model. Where one-year in advance forecasting of reservoir inflows can provide data to have an optimal reservoir operation, reports show we still need more accurate models which include all effective parameters to have more forecasting accuracy than traditional linear models (ARMA and ARIMA). Thus, hybridization of models was employed for improving the accuracy of flow forecasting. Moreover, various forecasters including large-scale climate signals were tested to promote forecasting. This paper focuses on testing MARMA-NARX hybrid model to enhance the accuracy of monthly inflow forecasts. Since the inflow in different periods of the year has in linear and non-linear trends, the hybrid model is proposed as a means of combining linear model, monthly autoregressive moving average (MARMA), and non-linear model, nonlinear autoregressive model with exogenous (NARX) inputs to upgrade the accuracy of flow forecasting. The results of the study showed enhanced forecasting accuracy through using the hybrid model.

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21

Zhang, Yechi, Jianzhou Wang, and Haiyan Lu. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting." Energies 12, no. 10 (May 20, 2019): 1931. http://dx.doi.org/10.3390/en12101931.

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Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.

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22

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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23

Voloshchuk, Roman V. "Analysis, Assessment and Forecasting of the Demographic Sphere of Economic Security." Control Systems and Computers, no. 6 (290) (December 2020): 35–45. http://dx.doi.org/10.15407/csc.2020.06.035.

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The performed forecasting of indicators of the state of the demographic sphere of Ukraine using the methods of adaptive forecasting, ARIMA and exponential smoothing speaks of greater accuracy and efficiency of ARIMA and adaptive forecasting, using these methods ineffective management decisions in the demographic sphere of Ukraine, while adaptive forecasting was more accurate when forecasting certain indicators of the demographic security sphere.

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Jin, Xue-Bo, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, and Seng Lin. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization." Energies 14, no. 6 (March 13, 2021): 1596. http://dx.doi.org/10.3390/en14061596.

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Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

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25

Vivas, Eliana, Héctor Allende-Cid, and Rodrigo Salas. "A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score." Entropy 22, no. 12 (December 15, 2020): 1412. http://dx.doi.org/10.3390/e22121412.

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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

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Song, Haiyan, Stephen F. Witt, and Thomas C. Jensen. "Tourism forecasting: accuracy of alternative econometric models." International Journal of Forecasting 19, no. 1 (January 2003): 123–41. http://dx.doi.org/10.1016/s0169-2070(01)00134-0.

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27

Aroian, Leo A. "The Forecasting Accuracy of Major Time Series." Technometrics 27, no. 4 (November 1985): 442–43. http://dx.doi.org/10.1080/00401706.1985.10488085.

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28

Carbone, Robert, and Wilpen L. Gorr. "ACCURACY OF JUDGMENTAL FORECASTING OF TIME SERIES." Decision Sciences 16, no. 2 (April 1985): 153–60. http://dx.doi.org/10.1111/j.1540-5915.1985.tb01480.x.

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29

Galbreth, Michael R., Mümin Kurtuluş, and Mikhael Shor. "How collaborative forecasting can reduce forecast accuracy." Operations Research Letters 43, no. 4 (July 2015): 349–53. http://dx.doi.org/10.1016/j.orl.2015.04.006.

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30

Witt, Christine A., and Stephen F. Witt. "An overview of tourism forecasting accuracy comparisons." Tourist Review 48, no. 2 (February 1993): 29–35. http://dx.doi.org/10.1108/eb058122.

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31

Kumar, Manmohan S. "Forecasting Accuracy of Crude Oil Futures Prices." IMF Working Papers 91, no. 93 (1991): 1. http://dx.doi.org/10.5089/9781451951110.001.

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32

Beckett-Camarata, Jane. "Revenue forecasting accuracy in Ohio local governments." Journal of Public Budgeting, Accounting & Financial Management 18, no. 1 (March 2006): 77–99. http://dx.doi.org/10.1108/jpbafm-18-01-2006-b004.

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33

Joon Chien, Doh. "Accuracy of Expenditure Forecasting: The Singapore Experience." Asian Journal of Public Administration 17, no. 2 (December 1995): 345–60. http://dx.doi.org/10.1080/02598272.1995.10800312.

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34

Stonebraker, Peter W., and Pricha Pantumsinchai. "Improving Forecasting Accuracy through Trading Day Adjustment." International Journal of Operations & Production Management 9, no. 1 (January 1989): 45–56. http://dx.doi.org/10.1108/eum0000000001217.

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Mills, Anthony, David Harris, and Martin Skitmore. "The accuracy of housing forecasting in Australia." Engineering, Construction and Architectural Management 10, no. 4 (August 2003): 245–53. http://dx.doi.org/10.1108/09699980310489951.

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36

Vu, Jo C. "Effect of Demand Volume on Forecasting Accuracy." Tourism Economics 12, no. 2 (June 2006): 263–76. http://dx.doi.org/10.5367/000000006777637412.

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37

van Doorn, Jozef W. M. "Forecasting accuracy of major time series methods." Futures 17, no. 1 (February 1985): 82–85. http://dx.doi.org/10.1016/0016-3287(85)90105-3.

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He, Ling T., and Chenyi Hu. "Midpoint method and accuracy of variability forecasting." Empirical Economics 38, no. 3 (March 19, 2009): 705–15. http://dx.doi.org/10.1007/s00181-009-0286-6.

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Schmidbauer, Harald, Angi Rösch, Tolga Sezer, and Vehbi Sinan Tunalioğlu. "Robust trading rule selection and forecasting accuracy." Journal of Systems Science and Complexity 27, no. 1 (February 2014): 169–80. http://dx.doi.org/10.1007/s11424-014-3302-7.

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40

Erickson, Gary M. "Marketing managers need more than forecasting accuracy." International Journal of Forecasting 3, no. 3-4 (January 1987): 453–55. http://dx.doi.org/10.1016/0169-2070(87)90040-9.

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Moiseev, S. N. "Accuracy of ES-layer screening-frequency forecasting." Radiophysics and Quantum Electronics 36, no. 2 (February 1993): 80–82. http://dx.doi.org/10.1007/bf01059487.

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Shetty, Dileep Kumar, and B. Ismail. "Hybrid Model Approach for Accuracy in Forecasting." Journal of the Indian Society for Probability and Statistics 19, no. 2 (September 8, 2018): 417–35. http://dx.doi.org/10.1007/s41096-018-0051-2.

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Vaidya, Rashesh. "Accuracy of Moving Average Forecasting for NEPSE." Journal of Nepalese Business Studies 13, no. 1 (December 31, 2020): 62–76. http://dx.doi.org/10.3126/jnbs.v13i1.34706.

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A simple moving average is one of the oldest and the simplest techniques of forecasting the trends of the stock market. The technical analysts follow mainly three types of moving averages, namely; simple, weighted, and exponential moving averages. Among these three types, as per the interest of investors, short-term and long-term time duration is used to calculate the trend using the moving average. All the mentioned moving averages are used by investors or analysts to predict the future trends of the market using historical data. Hence, for evaluating their forecasting accuracy, the paper has used both the short-term and the long-term moving average. The paper has used the NEPSE (closing) index values to calculate as well as plotted the moving averages to forecast the future trend and its accuracy with the help of Mean Absolute Percentage Error (MAPE). The paper found that there is a better crossover in the graphical representation of the moving average in the long-term moving average. In context to the Nepalese stock market, the MAPE results reflected a weekly (5-trading days) 5-SMA analysis of the market movement as the most relevant in short-term forecasting. Similarly, using the technique of moving average, 200-SMA (200-trading days of a year) was seen as the most effective to forecast long-term trends. The result of the long-term moving average MAPE pointed out that the annual reports of the listed companies better determine the trend of the market.

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Jeffrey, Benjamin, David M. Aanensen, Nicholas J. Croucher, and Samir Bhatt. "Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling." Wellcome Open Research 5 (August 19, 2020): 194. http://dx.doi.org/10.12688/wellcomeopenres.16153.1.

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Background: Increasing antibiotic resistance in a location may be mitigated by changes in treatment policy, or interventions to limit transmission of resistant bacteria. Therefore, accurate forecasting of the distribution of antibiotic resistance could be advantageous. Two previously published studies addressed this, but neither study compared alternative forecasting algorithms or considered spatial patterns of resistance spread. Methods: We analysed data describing the annual prevalence of antibiotic resistance per country in Europe from 2012 – 2016, and the quarterly prevalence of antibiotic resistance per clinical commissioning group in England from 2015 – 2018. We combined these with data on rates of possible covariates of resistance. These data were used to compare the previously published forecasting models, with other commonly used forecasting models, including one geospatial model. Covariates were incorporated into the geospatial model to assess their relationship with antibiotic resistance. Results: For the European data, which was recorded on a coarse spatiotemporal scale, a naïve forecasting model was consistently the most accurate of any of the forecasting models tested. The geospatial model did not improve on this accuracy. However, it did provide some evidence that antibiotic consumption can partially explain the distribution of resistance. The English data were aggregated at a finer scale, and expected-trend-seasonal (ETS) forecasts were the most accurate. The geospatial model did not significantly improve upon the median accuracy of the ETS model, but it appeared to be less sensitive to noise in the data, and provided evidence that rates of antibiotic prescription and bacteraemia are correlated with resistance. Conclusion: Annual, national-level surveillance data appears to be insufficient for fitting accurate antibiotic resistance forecasting models, but there is evidence that data collected at a finer spatiotemporal scale could be used to improve forecast accuracy. Additionally, incorporating antibiotic prescription or consumption data into the model could improve the predictive accuracy.

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Eroshenko, Stanislav A., Alexandra I. Khalyasmaa, Denis A. Snegirev, Valeria V. Dubailova, Alexey M. Romanov, and Denis N. Butusov. "The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation." Applied Sciences 10, no. 22 (November 21, 2020): 8265. http://dx.doi.org/10.3390/app10228265.

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The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.

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Chen, Yan Ping, and Yan Li Chang. "The Factors Affecting Accuracy Rate of Power Load Forecasting in Summer." Advanced Materials Research 1049-1050 (October 2014): 617–20. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.617.

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This paper analysis the low power load forecasting accuracy in summer deep. It found the factors affecting accuracy rate of power load forecasting in summer, and proposed the measures to increase the load forecasting accuracy.

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Bratten, Brian, Cristi A. Gleason, Stephannie A. Larocque, and Lillian F. Mills. "Forecasting Taxes: New Evidence from Analysts." Accounting Review 92, no. 3 (August 1, 2016): 1–29. http://dx.doi.org/10.2308/accr-51557.

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ABSTRACT We provide new evidence about how analysts incorporate and improve on management ETR forecasts. Quarterly ETR reporting under the integral method provides mandatory point-estimate forecasts by management, but firms must record certain “discrete” tax items fully in the quarter in which they occur, polluting these forecasts. We investigate management ETR accuracy, analysts' decisions to mimic management's estimate, analysts' accuracy relative to each other or to management, and dispersion. Our comprehensive analysis reveals that analysts deviate from management more and are more accurate relative to management as complexity increases, with real effects on EPS accuracy and dispersion. In contrast to prior research that analysts ignore or are confused by taxes, we provide evidence that analysts pay attention to taxes and improve on management estimates. Based on our evidence that management's quarterly ETRs have less predictive value in the presence of discrete items, we suggest standard-setters reexamine the discrete item exception to require more disclosure.

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Rahman, Khalilur, Margaretha Ari Anggorowati, and Agung Andiojaya. "Prediction and Simulation Spatio-Temporal Support Vector Regression for Nonlinear Data." International Journal on Information and Communication Technology (IJoICT) 6, no. 1 (June 20, 2020): 31. http://dx.doi.org/10.21108/ijoict.2020.61.477.

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<p>Spatio-temporal model forecasting method is a forecasting model that combines forecasting with a function of time and space. This method is expected to be able to answer the challenge to produce more accurate and representative forecasting. Using the ability of method Support Vector Regression in dealing with data that is mostly patterned non-linear premises n adding a spatial element in the model of forecasting in the form of a model forecasting Spatio- Temporal. Some simulations have done with generating data that follows the Threshold Autoregressive model. The models are correlated into spatial points generated by several sampling methods. Simulation models are generated to comparing the accuracy between model Spatio-Temporal Support Vector Regression and model ARIMA based on Mean Error, Mean Average Error, Root Mean Square Error, and Mean Average Percentage Error. Based on the evaluation results, it is shown that forecasting with the Spatio-Temporal Support Vector Regression model has better accuracy than forecasting ARIMA.</p>

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Kim, Hyo Young, Yun Shin Lee, and Duk Bin Jun. "The effect of relative performance feedback on judgmental forecasting accuracy." Management Decision 57, no. 7 (July 8, 2019): 1695–711. http://dx.doi.org/10.1108/md-06-2017-0549.

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Purpose Forecasting processes in organizational settings largely rely on human judgment, which makes it important to examine ways to improve the accuracy of these judgmental forecasts. The purpose of this paper is to test the effect of providing relative performance feedback on judgmental forecasting accuracy. Design/methodology/approach This paper is based on a controlled laboratory experiment. Findings The authors show that feedback that ranks the forecasting performance of participants improves their accuracy compared with the forecasting accuracy of participants who do not get such feedback. The authors also find that the effectiveness of such relative performance feedback depends on the content of the feedback information as well as on whether accurate forecasting performance is linked to additional financial rewards. Relative performance feedback becomes more effective when subjects are told they rank behind other participants than when they are told they rank higher than other participants. This finding is consistent with loss aversion: low-ranked individuals view their performance as a loss and work harder to avoid it. By contrast, top performers tend to slack off. Finally, the authors find that the addition of monetary rewards for top performers reduces the effectiveness of relative performance feedback, particularly for individuals whose performance ranks near the bottom. Originality/value One way to improve forecasting accuracy when forecasts rely on human judgment is to design an effective incentive system. Despite the crucial role of judgmental forecasts in organizations, little attention has been devoted to this topic. The aim of this study is to add to the literature in this field.

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Wang, Fei, Zhao Zhen, Chun Liu, Zengqiang Mi, Miadreza Shafie-khah, and João Catalão. "Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization." Energies 11, no. 1 (January 12, 2018): 184. http://dx.doi.org/10.3390/en11010184.

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Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model’s performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

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