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Journal articles on the topic 'Forecasting strategies'

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Published: 4 June 2021
Last updated: 18 February 2022

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1

Bar-On, Raphael R. "Forecasting Tourism Demand: Methods and Strategies." Annals of Tourism Research 30, no. 3 (July 2003): 754–56. http://dx.doi.org/10.1016/s0160-7383(03)00051-3.

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2

Junk, Constantin, Luca Delle Monache, Stefano Alessandrini, Guido Cervone, and Lueder von Bremen. "Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble." Meteorologische Zeitschrift 24, no. 4 (July 21, 2015): 361–79. http://dx.doi.org/10.1127/metz/2015/0659.

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3

Rožanec, Jože M., Blaž Kažič, Maja Škrjanc, Blaž Fortuna, and Dunja Mladenić. "Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies." Applied Sciences 11, no. 15 (July 23, 2021): 6787. http://dx.doi.org/10.3390/app11156787.

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Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.
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4

Kaiser, Mark J. "Multiple well lease decomposition and forecasting strategies." Journal of Petroleum Science and Engineering 116 (April 2014): 59–71. http://dx.doi.org/10.1016/j.petrol.2014.02.016.

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5

Savio, Nicolas, and Konstantinos Nikolopoulos. "Forecasting the Effectiveness of Policy Implementation Strategies." International Journal of Public Administration 33, no. 2 (January 13, 2010): 88–97. http://dx.doi.org/10.1080/01900690903241765.

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SCHWARZKOPF, ALBERT B., RICHARD J. TERSINE, and JOHN S. MORRIS. "Top-down versus bottom-up forecasting strategies." International Journal of Production Research 26, no. 11 (November 1988): 1833–43. http://dx.doi.org/10.1080/00207548808947995.

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Moniz, Nuno, Paula Branco, and Luís Torgo. "Resampling strategies for imbalanced time series forecasting." International Journal of Data Science and Analytics 3, no. 3 (February 16, 2017): 161–81. http://dx.doi.org/10.1007/s41060-017-0044-3.

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Hadri, Sarah, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, and Mohamed El Arroussi. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings." Energies 14, no. 18 (September 15, 2021): 5831. http://dx.doi.org/10.3390/en14185831.

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In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.
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Bakri, Rizal, Umar Data, and Andika Saputra. "Marketing Research : The Application of Auto Sales Forecasting Software to Optimize Product Marketing Strategies." Journal of Applied Science, Engineering, Technology, and Education 1, no. 1 (November 10, 2019): 6–12. http://dx.doi.org/10.35877/454ri.asci1124.

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The aims of this study is to apply the Auto Sales Forecasting software to predict sales transaction data. The Auto Sales Forecasting software consists of two main features namely descriptive analysis and forcasting features along with its visualization. Forecasting methods contained in the Auto Sales Forecasting application are forecasting methods of Simple Moving Average, Robust Exponantial Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. The Auto Sales Forecasting software can intelligently choose the best forecasting method based on RMSE values. The results showed that the Auto Sales Forecasting software successfully analyzed the sales transaction data. From the analysis it was found that there were 43 types of products produced and sold by the Futry Bakery & Cake Store. Three of them are the types of products that are most in demand by consumers, namely Sweet Bread, Maros Bread, and Traditional Cakes 3500. The best selling product type, Sweet Bread, is used to build forecasting models. The best forecasting method is the Robust Exponential Smoothing method with the smallest RMSE value of 0.83 on the variable number of sold out products. Forecasting results using the Robust Exponantial Smoothing method show that the average number of products to sell for the next seven days ranges from 116 products with a certain confidence interval value.
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10

Mellers, Barbara, Lyle Ungar, Jonathan Baron, Jaime Ramos, Burcu Gurcay, Katrina Fincher, Sydney E. Scott, et al. "Psychological Strategies for Winning a Geopolitical Forecasting Tournament." Psychological Science 25, no. 5 (March 21, 2014): 1106–15. http://dx.doi.org/10.1177/0956797614524255.

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11

Yan, Ruiliang, and Kai Yu Wang. "Market forecasting information and firm pricing-advertising strategies." International Journal of Information and Decision Sciences 1, no. 4 (2009): 382. http://dx.doi.org/10.1504/ijids.2009.027758.

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12

Dempsey, Mark J. "Forecasting Strategies for Haboobs: An Underreported Weather Phenomenon." Advances in Meteorology 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/904759.

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On June 5, 2013, Lubbock Texas is hit by a haboob at 10:30 pm. The storm is categorized as a wind event by television media and the dust component goes unreported. This event is used as a case study to evaluate the usefulness of the polarimetric variables differential reflectivity (ZDR) and correlation coefficient (CC) in identifying the storm as a haboob. Photographic evidence of the haboob is collected and correlated to NEXRAD signatures of base reflectivity and velocity from the Lubbock TX NEXRAD station (KLBB). NEXRAD level III products ZDR and CC are also obtained. The storm presents with gust front features to the north and east of the station. Low values returned from CC indicate nonmeteorological content. ZDR representations weakly indicate the presence of gust fronts to the east, with a stronger signal to the north. As no visual evidence of the northern gust front is available, the ZDR data are inconclusive. The correlation of low CC values to the visual representation of the haboob is an indicator that CC in combination with the NEXRAD base reflectivity and velocity products may be used to test wind events for the presence of sand, dust, and dirt and therefore exhibit predictive qualities.
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Aiolfi, Marco, and Allan Timmermann. "Persistence in forecasting performance and conditional combination strategies." Journal of Econometrics 135, no. 1-2 (November 2006): 31–53. http://dx.doi.org/10.1016/j.jeconom.2005.07.015.

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Oliveira, Mariana, Nuno Moniz, Luís Torgo, and Vítor Santos Costa. "Biased resampling strategies for imbalanced spatio-temporal forecasting." International Journal of Data Science and Analytics 12, no. 3 (June 21, 2021): 205–28. http://dx.doi.org/10.1007/s41060-021-00256-2.

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Ringnes, Hege Kristin, Gry Stålsett, Harald Hegstad, and Lars Johan Danbolt. "Emotional Forecasting of Happiness." Archive for the Psychology of Religion 39, no. 3 (December 2017): 312–43. http://dx.doi.org/10.1163/15736121-12341341.

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The aim of this study was to explore which group-based emotion regulation goals and strategies are offered in the group culture of Jehovah's Witnesses (JWS). Based on interviews with 29 group-active JWS in Norway, a thematic analysis was conducted in which an overall pattern of cognition taking precedence over emotions was found. Due to endtime expectations and a long-term goal of eternal life in Paradise, future emotions were prioritized. The emotion regulation strategies identified among JWS were social sharing and the interconnected cognitive reappraisal. A new concept, emotional forecasting, was introduced, describing a reappraisal tactic of regulation using prospects of future emotions to regulate the here and now. It was concluded that the prospection of the future is a strong regulator of emotions of the here and now and should be included in psychological models of emotion regulation.
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Kim, Minsoo, Kangsan Kim, Hyungeun Choi, Seonjeong Lee, and Hongseok Kim. "Practical Operation Strategies for Energy Storage System under Uncertainty." Energies 12, no. 6 (March 21, 2019): 1098. http://dx.doi.org/10.3390/en12061098.

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Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method.
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17

Vassallo, Daniel, Raghavendra Krishnamurthy, Thomas Sherman, and Harindra J. S. Fernando. "Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting." Energies 13, no. 20 (October 20, 2020): 5488. http://dx.doi.org/10.3390/en13205488.

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Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ∼8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.
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18

Ouyang, Zuokun, Philippe Ravier, and Meryem Jabloun. "STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones." Engineering Proceedings 5, no. 1 (July 8, 2021): 42. http://dx.doi.org/10.3390/engproc2021005042.

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This paper aims at comparing different forecasting strategies combined with the STL decomposition method. STL is a versatile and robust time series decomposition method. The forecasting strategies we consider are as follows: three statistical methods (ARIMA, ETS, and Theta), five machine learning methods (KNN, SVR, CART, RF, and GP), and two versions of RNNs (CNN-LSTM and ConvLSTM). We conduct the forecasting test on six horizons (1, 6, 12, 18, and 24 months). Our results show that, when applied to monthly industrial M3 Competition data as a preprocessing step, STL decomposition can benefit forecasting using statistical methods but harms the machine learning ones. Moreover, the STL-Theta combination method displays the best forecasting results on four over the five forecasting horizons.
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19

Qin, Juanjuan, Liguo Ren, and Liangjie Xia. "Carbon Emission Reduction and Pricing Strategies of Supply Chain under Various Demand Forecasting Scenarios." Asia-Pacific Journal of Operational Research 34, no. 01 (February 2017): 1740005. http://dx.doi.org/10.1142/s021759591740005x.

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This study incorporates consumer’s low carbon awareness (CLA) and demand forecasting into supply chains that adopt the cap-and-trade system. Three demand forecasting scenarios are discussed, namely, information sharing, full information sharing, and retailer-only forecasting. Strategies for pricing and reduction of equilibrium of carbon emission are derived. We also compare the decisions and profits in the three cases and present numerical analysis.
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20

Rötheli, Tobias F. "Forecasting among alternative strategies in the management of uncertainty." Managerial and Decision Economics 19, no. 3 (May 1998): 179–87. http://dx.doi.org/10.1002/(sici)1099-1468(199805)19:3<179::aid-mde878>3.0.co;2-k.

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21

Wasserman, Gary S., and Agus Sudjianto. "A Comparison of Three Strategies for Forecasting Warranty Claims." IIE Transactions 28, no. 12 (December 1996): 967–77. http://dx.doi.org/10.1080/15458830.1996.11770751.

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22

Papailias, Fotis, Dimitrios D. Thomakos, and Jiadong Liu. "The Baltic Dry Index: cyclicalities, forecasting and hedging strategies." Empirical Economics 52, no. 1 (April 6, 2016): 255–82. http://dx.doi.org/10.1007/s00181-016-1081-9.

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23

Bails, Dale. "Sales forecasting: Timesaving and profit-making strategies that work." International Journal of Forecasting 2, no. 2 (January 1986): 250–51. http://dx.doi.org/10.1016/0169-2070(86)90125-1.

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24

Sari, Nadia Roosmalita, Wayan Firdaus Mahmudy, and Aji Prasetya Wibawa. "Evolution strategies based coefficient of TSK fuzzy forecasting engine." International Journal of Advances in Intelligent Informatics 7, no. 1 (March 31, 2021): 89. http://dx.doi.org/10.26555/ijain.v7i1.376.

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Forecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evolutionary algorithm with good performance optimization problems. ES algorithm used to determine the best coefficient values on consequent fuzzy rules. This research uses Bank Indonesia time-series data as in the previous study. ES algorithm uses the popSize test to determine the number of initial chromosomes to produce the best optimal solution for this problem. The increase of popSize creates better fitness value due to the ES's broader search area. The RMSE of ES-TSK is 0.637, which outperforms the baseline approach. This research generally shows that ES may reduce repetitive experiment events due to Fuzzy coefficients' manual setting. The algorithm complexity may cost to the computing time, yet with higher performance.
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Abdelmalek, Wafa, Sana Ben Hamida, and Fathi Abid. "Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming." Journal of Applied Mathematics and Decision Sciences 2009 (August 31, 2009): 1–19. http://dx.doi.org/10.1155/2009/179230.

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The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.
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Modaresi, Fereshteh, Shahab Araghinejad, and Kumars Ebrahimi. "Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting." Journal of Hydroinformatics 20, no. 4 (February 21, 2018): 917–33. http://dx.doi.org/10.2166/hydro.2018.098.

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Abstract Monthly streamflow forecasting plays an important role in water resources management, especially for dam operation. In this paper, an approach of model fusion technique named selected model fusion (SMF) is applied and assessed under two strategies of model selection in order to improve the accuracy of streamflow forecasting. The two strategies of SMF are: fusion of the outputs of best individual forecasting models (IFMs) selected by dendrogram analysis (S1), and fusion of the best outputs of all IFMs resulting from an ordered selection algorithm (S2). In both strategies, five data-driven models including: artificial neural network, generalized regression neural network, least square-support vector regression, K-nearest neighbor regression, and multiple linear regression with optimized structure are performed as IFMs. The SMF strategies are applied for forecasting the monthly inflow to Karkheh reservoir, Iran, owning various patterns between predictor and predicted variables in different months. Results show that applying SMF approach based on both strategies results in more accurate forecasts in comparison with fusion of all IFMs outputs (S3), as the benchmark. However, comparison of the two SMF strategies reveals that the implementation of strategy (S2) considerably improves the accuracy of forecasts than strategy (S1) as well as the best IFM results (S4) in all months.
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Mariano-Hernández, Deyslen, Luis Hernández-Callejo, Felix Santos García, Oscar Duque-Perez, and Angel L. Zorita-Lamadrid. "A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics." Applied Sciences 10, no. 23 (November 24, 2020): 8323. http://dx.doi.org/10.3390/app10238323.

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Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.
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Valiulis, Donatas. "METHODS OF FORECASTING IN FOREIGN EXCHANGE MARKET." Mokslas - Lietuvos ateitis 2, no. 4 (August 31, 2010): 69–72. http://dx.doi.org/10.3846/mla.2010.074.

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The study deals with the interaction of technical and fundamental analysis in forecasting the foreign exchange market. Different forecasting approaches for market participants were discussed and forecasting strategies analyzed. Pressing problems, further areas of research and identified trends to investigations in the future were also reviewed.
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29

Krauße, T., J. Cullmann, P. Saile, and G. H. Schmitz. "Robust multi-objective calibration strategies – possibilities for improving flood forecasting." Hydrology and Earth System Sciences 16, no. 10 (October 15, 2012): 3579–606. http://dx.doi.org/10.5194/hess-16-3579-2012.

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Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008) studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE). This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify robust model parameter vectors with respect to one single objective. The consideration of multiple objectives is just possible by aggregation. In this paper, we present an approach that combines the principles of multi-objective optimisation and depth-based sampling, entitled Multi-Objective Robust Parameter Estimation (MOROPE). It applies a multi-objective optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently, it samples parameter vectors with high data depth using a further developed sampling algorithm presented in Krauße and Cullmann (2012a). We study the effectivity of the proposed method using synthetical test functions and for the calibration of a distributed hydrologic model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland.
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Krauße, T., J. Cullmann, P. Saile, and G. H. Schmitz. "Robust multi-objective calibration strategies – chances for improving flood forecasting." Hydrology and Earth System Sciences Discussions 8, no. 2 (April 15, 2011): 3693–741. http://dx.doi.org/10.5194/hessd-8-3693-2011.

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Abstract. Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless whether the objective is aggregated of several criteria that measure different (possibly opposite) aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. One possible approach to estimate the Pareto set effectively and efficiently is the particle swarm optimisation (PSO). It has already been successfully applied in various other fields and has been reported to show effective and efficient performance. Krauße and Cullmann (2011b) presented a method entitled ROPEPSO which merges the strengths of PSO and data depth measures in order to identify robust parameter vectors for hydrological models. In this paper we present a multi-objective parameter estimation algorithm, entitled the Multi-Objective Robust Particle Swarm Parameter Estimation (MO-ROPE). The algorithm is a further development of the previously mentioned single-objective ROPEPSO approach. It applies a newly developed multi-objective particle swarm optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently it samples robust parameter vectors by the application of data depth metrics. In a preliminary assessment MO-PSO-GA is compared with other multi-objective optimisation algorithms. In the frame of a real world case study MO-ROPE is applied identifying robust parameter vectors of a distributed hydrological model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland. The method is compared with existing robust parameter estimation methods.
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31

V’yugin, V. V., and V. G. Trunov. "Applications of combined financial strategies based on universal adaptive forecasting." Automation and Remote Control 77, no. 8 (August 2016): 1428–46. http://dx.doi.org/10.1134/s0005117916080099.

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32

Savio, Nicolas D., and Konstantinos Nikolopoulos. "Forecasting effectiveness of policy implementation strategies: working with semi‐experts." Foresight 11, no. 6 (October 14, 2009): 86–93. http://dx.doi.org/10.1108/14636680911004984.

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33

Thomas, R. W. "Forecasting Global HIV—AIDS Dynamics: Modelling Strategies and Preliminary Simulations." Environment and Planning A: Economy and Space 26, no. 7 (July 1994): 1147–66. http://dx.doi.org/10.1068/a261147.

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In the light of the considerable biomathematical effort devoted to building models of the incidence of HIV and AIDS in communities, in this paper a multiregion specification is developed that includes a parsimonious cross-infection mechanism where high-risk and low-risk populations are distinguished by their promiscuity rates. The nature of this mixing is compared with some existing modelling formats, and some preliminary simulations are presented for the timing and spread of the epidemic in a sixteen-city global system.
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Kwan, Josephine W. C., K. Lam, Mike K. P. So, and Philip L. H. Yu. "Forecasting and trading strategies based on a price trend model." Journal of Forecasting 19, no. 6 (2000): 485–98. http://dx.doi.org/10.1002/1099-131x(200011)19:6<485::aid-for759>3.0.co;2-p.

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Goodwin, P., and G. Wright. "Heuristics, biases and improvement strategies in judgmental time series forecasting." Omega 22, no. 6 (November 1994): 553–68. http://dx.doi.org/10.1016/0305-0483(94)90047-7.

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O’Mara, Martha A. "Strategies for demand forecasting in corporate real estate portfolio management." Journal of Corporate Real Estate 2, no. 2 (April 2000): 123–37. http://dx.doi.org/10.1108/14630010010811248.

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Mawartika, Yayang Eluis Bali, Azhari SN, and Agus Sihabuddin. "TOPSIS and SLR methods on the Decision Support System for Selection the Management Strategies of Funeral Land." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 2 (April 30, 2019): 169. http://dx.doi.org/10.22146/ijccs.39788.

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The funeral land is one of the public facilities that must be provided by Local Government to support community activities. The need for funeral land in Lubuklinggau continues to increase while the availability of funeral land is decreasing, this is because the number of deaths of the population continues to increase every year. Forecasting the land availability of funeral for the coming year and applying the management strategies of funeral land can overcome the needs of the cemetery. Forecasting the land availability of funeral using Simple Linear Regression. TOPSIS to choose the management strategies of funeral land. Forecasting uses two variables that are the variable number of the population deaths and the variable amount of funeral land in the last 5 years. Forecasting results will be used as one of the assessment criteria in the decision support system for selection of the management strategies of funeral land. The alternative of the funeral management strategy that will be applied and assessed in accordance with Local Regulation of Town of Lubuklinggau. The highest value of the end result of the system will be used as a recommendation for the selection of management strategies.
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38

Kim, Dayoon, Jin Won Mun, Daniel Jin Won Kim, and Soo Hyun Ahn. "Market Predictor: Game Theory Model Forecasting Consumer Choice through Analysis of Simultaneous Marketing Strategies and Consumer Behavior." International Journal of Trade, Economics and Finance 8, no. 3 (June 2017): 165–68. http://dx.doi.org/10.18178/ijtef.2017.8.3.556.

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Brorsen, B. Wade, and Scott H. Irwin. "Improving the Relevance of Research on Price Forecasting and Marketing Strategies." Agricultural and Resource Economics Review 25, no. 1 (April 1996): 68–75. http://dx.doi.org/10.1017/s1068280500000095.

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Agricultural economists' research on price forecasting and marketing strategies has been used little by those in the real world. We argue that fresh approaches to research are needed. First, we argue that we need to adopt a new theoretical paradigm, noisy rational expectations. This paradigm suggests that gains from using price forecasting models with public data or from using a marketing strategy are not impossible, but any gains are likely to be small. We need to conduct falsification tests; to perform confirmation and replication; to adjust research to reflect structural changes, such as increased contracting; and always to conduct statistical tests. We also provide a modest agenda for changing our research and extension programs.
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Mutum, Kelvin. "Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index." Vision: The Journal of Business Perspective 24, no. 2 (April 26, 2020): 217–27. http://dx.doi.org/10.1177/0972262920914117.

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The present study was to examine whether the performance of options trading strategies can be improved if volatility forecasting incorporating investors’ sentiment was incorporated in the decision-making process at the Indian options market. The study adopted the multiple-factor model to build the Indian volatility forecasting model. The benchmark forecasting model (BMF) includes absolute daily returns (|RA|), daily high–low range (HLR) and daily realized volatility (RV). The proxies of investors’ sentiment considered in the study were India volatility index (IVIX), advance decline ratio (ADR), put-call open interest (PCOI) and their changes. The results of the causality and regression test indicate that investors’ sentiment and their changes should be included in the forecasting model. Mean absolute percentage error (MAPE) indicates that 15-day holding period shows the minimum error. Straddle strategies were simulated 15 days ahead before the options maturity date base on the direction of the forecast for different volatility forecasting models. The simulation result shows that the options trading performance might be improved if volatility forecasting incorporating investor sentiment, particularly IVIX, was incorporated in the decision-making process at the Indian options market. From the behavioural finance point of view, the study bridges the gap between options trading, volatility forecasting and information content of investors’ sentiment at the Indian financial market.
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DOĞAN, Erdem. "Performance analysis of LSTM model with multi-step ahead strategies for a short-term traffic flow prediction." Scientific Journal of Silesian University of Technology. Series Transport 111 (June 30, 2021): 15–31. http://dx.doi.org/10.20858/sjsutst.2021.111.2.

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In this study, the effect of direct and recursive multi-step forecasting strategies on the short-term traffic flow forecast performance of the Long Short-Term Memory (LSTM) model is investigated. To increase the reliability of the results, analyses are carried out with various traffic flow data sets. In addition, databases are clustered using the k-means++ algorithm to reduce the number of experiments. Analyses are performed for different time periods. Thus, the contribution of strategies to LSTM was examined in detail. The results of the recursive based strategy performances are not satisfactory. However, different versions of the direct strategy performed better at different time periods. This research makes an important contribution to clarifying the compatibility of LSTM and forecasting strategies. Thus, more efficient traffic flow prediction models will be developed and systems such as Intelligent Transportation System (ITS) will work more efficiently. A practical implication for researchers that forecasting strategies should be selected based on time periods.
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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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Ene, Seval, and Nursel Öztürk. "Managing return flow of end-of-life products for product recovery operations." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 169–77. http://dx.doi.org/10.18844/gjbem.v7i1.1393.

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Increased consciousness on environment and sustainability, leads companies to apply environmentally friendly strategies such as product recovery and product return management. These strategies are generally applied in reverse logistics concept. Implementing reverse logistics successfully becomes complicated for companies due to uncertain parameters of the system like quantity, quality and timing of returns. A forecasting methodology is required to overcome these uncertainties and manage product returns. Accurate forecasting of product return flows provides insights to managers of reverse logistics. This paper proposes a forecasting model based on grey modelling for managing end-of-life products’ return flow. Grey models are capable for handling data sets characterized by uncertainty and small sized. The proposed model is applied to data set of a specific end-of-life product. Attained results show that the proposed forecasting model can be successfully used as a forecasting tool for product returns and a supportive guidance can be provided for future planning. Keywords: End-of-life products, grey modelling, product return flow, product recovery;
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44

Lascsáková, Marcela. "INFLUENCE OF TWO DIFFERENT ACCURACY IMPROVEMENTS TO NUMERICAL PRICE FORECASTING." Acta logistica 7, no. 4 (December 31, 2020): 253–60. http://dx.doi.org/10.22306/al.v7i4.187.

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The paper aims to compare two different strategies of accuracy improvement of studied prognostic numerical models. The price prognoses of aluminium on the London Metal Exchange were determined as the numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate two ideas were realized, the modification of the initial condition value by the nearest stock exchange (initial condition drift) and different way of creation of the differential equation in solved Cauchy initial problem (using two known initial values). With regard to the accuracy of the determined numerical models, the model using two known initial values obtained slightly better forecasting results. The mean absolute percentage error of all observed forecasting terms was mostly less than 5%. This strategy was more successful in problematic price movements, especially at steep price increase and within significant changes in the price movements. Larger fluctuation of prognoses calculated by this model was disadvantageous in forecasting terms with a small error. Moderate increase of prognoses obtained by the model using initial condition drift better described price fluctuation. Both chosen strategies eliminated the forecasting terms with the mean absolute percentage error larger than 10%. Therefore, we recommend both strategies as acceptable way for commodity price forecasting.
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Wang, Qianyang, Yuan Liu, Qimeng Yue, Yuexin Zheng, Xiaolei Yao, and Jingshan Yu. "Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China." Water 12, no. 12 (December 16, 2020): 3532. http://dx.doi.org/10.3390/w12123532.

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.
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Meng, Fanlin, Kui Weng, Balsam Shallal, Xiangping Chen, and Monjur Mourshed. "Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response." Proceedings 2, no. 15 (August 27, 2018): 1133. http://dx.doi.org/10.3390/proceedings2151133.

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In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the future research directions for energy optimization in the context of smart buildings and smart grids.
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Teguh, Muhammad, and Abdul Bashir. "Indonesia’s Economic Growth Forecasting." SRIWIJAYA INTERNATIONAL JOURNAL OF DYNAMIC ECONOMICS AND BUSINESS 3, no. 2 (July 10, 2019): 134. http://dx.doi.org/10.29259/sijdeb.v3i2.134-145.

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The high economic growth is very important for Indonesia to accelerate the development process at this period. Although, the growth rate was reached at 5.17 % in 2018 is likely high enough, some domestic economists even point out, it really can be raised to a higher level. This research tries to investigate and formulate again Indonesia’s economic growth rate in 2018 and forecast it for 2019. By doing analysis recent real GDP data by industrial origin and by type of expenditures, and also consider all of the available potential economic resources, this research shows that Indonesia’s economic growth rate could stand at 6.03 % in 2018 and also at 6.03 % in 2019. Anyway, the government need a good economic plan and consistently performing appropriate strategies which are suited to targets in order to have rapid and stable economic growth rate
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Морозова, Галина Александровна, and Елена Яковлевна Григорьева. "STRATEGIES FOR COMPREHENSION OF PROFESSIONALLY ORIENTED TEXTS: HEADER AS AN ELEMENT OF THE FORECASTING STRATEGY." Вестник Тверского государственного университета. Серия: Педагогика и психология, no. 4(53) (December 21, 2020): 204–12. http://dx.doi.org/10.26456/vtpsyped/2020.4.204.

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Рассмотрены вопросы обучения студентов неязыковых факультетов стратегиям понимания профессионально ориентированного текста с учетом выбранного профиля, а также личности студента, заинтересованного в получении профессиональных знаний, в том числе при использовании иностранных источников информации. Дано определение стратегии понимания и сделан акцент на стратегии прогнозирования. На примере стратегии прогнозирования, а именно анализе заголовка текста как элемента стратегии прогнозирования, наглядно продемонстрировано, что владение стратегиями ускоряет ментальные процессы восприятия и эффективность переработки информации. Прогнозирование, представляя собой актуализацию уже имеющихся знаний, является основным компонентом зрелого чтения. The article discusses the issues of teaching students of non-linguistic faculties strategies for comprehension a professionally oriented text, taking into account the profile of the specialty, as well as the personality of a student interested in obtaining professional knowledge, including of using foreign sources of information. The definition of the comprehension strategy is given and the emphasis is made on the forecasting strategy. Using the forecasting strategy as an example, namely the analysis of the text title as an element of the forecasting strategy,it is clearly demonstrated that mastering strategies accelerates and facilitates the mental processes of perception and the efficiency of information processing. Forecasting, being the actualization of existing knowledge, is the main component of mature reading.
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Fang, Yiling, Xinhui Wang, and Jinjiang Yan. "Green Product Pricing and Order Strategies in a Supply Chain under Demand Forecasting." Sustainability 12, no. 2 (January 18, 2020): 713. http://dx.doi.org/10.3390/su12020713.

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In this paper, we investigate price and order strategies for innovative green products using demand forecasting and sharing. We formulate the problem using a Stackelberg game and propose a dynamic contract that specifies an initial wholesale price, a minimum order quantity, a demand sharing agreement, and a decisions adjustment agreement. We arrived at the following main findings and implications. First, the manufacturer offers a higher or lower wholesale price than the initial one depending on the variation in the market status. Also, the retailer’s ordering decisions will increase with the wholesale price, which contradicts the common assumption that ordering decisions decrease with the wholesale price. Interestingly, if the market improves, the manufacturer obtains a higher profit margin than the retailer; if the market worsens, the manufacturer suffers more loss of profit margin than the retailer. Second, when the cost of information sharing is smaller than an upper bound, demand forecasting and sharing are always beneficial to the manufacturer. However, the value of demand forecasting and sharing for the retailer is significantly affected by the market status variation. Third, high information accuracy will not necessarily increase the profits of the manufacturer and the retailer, even if the market status is better than expected. Finally, numerical examples show the parameters’ effects. We have several main managerial insights. When the shared demand information is received from the retailer, the manufacturer can determine wholesale price strategies according to the retailer’s demand forecast. Moreover, if the manufacturer wants to ensure profitability, they should not choose retailers with a higher capability of demand forecasting.
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Acharjee, Santanu, and Binod Chandra Tripathy. "Strategies in Mixed Budget: A Bitopological Approach." New Mathematics and Natural Computation 15, no. 01 (December 25, 2018): 85–94. http://dx.doi.org/10.1142/s1793005719500054.

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The forecasting graphs of World Bank, Reserve Bank of India, etc. are mostly line graphs or time series graphs. Any forecasting contains “standard error” as an error with complicated statistical formulae. A keen observation shows that mathematical patterns are available in nature, but in most of the cases, it is difficult for us to recognize these patterns. Similarly, it is most important for us to know the least upper bounds of these line graphs or time series graphs so that peaks of the prices with respect to time will not exceed these least upper bounds. It is hard to find any statistical or mathematical tool to determine these least upper bounds. Thus we give methodology to obtain these least upper bounds. We show existence of an equilibrium between the expected price and the original price of a commodity with the help of local functions and expansion operators of a bitopological space. These methods are based on choice of a consumer. Examples are provided to show that price of a commodity cannot exceed the interval of expected price. Moreover, we try to provide possible answers to the problem of “Control of Economic Variable” of Morgenstern [O. Morgenstern, Thirteen critical points in contemporary economic theory: An interpretation, Journal of Economic Literature 10(4) 1972 1163–1189] by determining least upper bounds.
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