Photovoltaic power forecasting with a rough set combination method

One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner

Device identifier for pre-screening of depression assessment

Current depression pre-screening session is usually completed with a tool such as the Depression, Anxiety, and Stress Scale (DASS21) before proceeding with treatment. Development of a device for depression measurement is hoped to overcome the issues related to time management and paper waste during assessment process. The device may also be useful as an assistance to other assessment method in delivering the final diagnosis. Data input and output of DASS21 has been utilized for the system identification technique where analysis comparison was conducted using ARX and transfer function model. The result shows that the transfer function model of order 2 outperform ARX with best fit of 98.1%. A device identifier was developed in order to assess the level of depression by implementing the transfer function into the Arduino code to predict the result from the user input

Prediction of CO2 Emissions in Iran using Grey and ARIMA Models

The examination of economic aspects of gas emissions and its consequences is very important, especially in terms of its volume at the current increasing trend. Therefore, the prediction of air pollution emissions of carbon dioxide can give the correct direction to policies adopted.  Hence, studying and forecasting of gas emissions is necessary. The purpose of this paper is the prediction of CO2 emissions based on Grey System and Autoregressive Integrated Moving Average and comparison of these two methods by RMSE, MAE and MAPE metrics. The results show the more accuracy of Grey system forecasting rather than other methods of prediction.  Also, based on the estimated results, the amount of carbon dioxide emissions will reach up to 925.68 million tons in 2020 which shows an increase of 66 percent growth compared to 2010 which is highly significant. Keywords: Carbon Dioxide Emissions; Forecasting; Grey system; Iran JEL Classifications: C22; C53; Q5

An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK

Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric models, artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, a brief review of a relatively new approach, known as grey system theory is provided. The paper offers an alternative approach to estimating the unknown parameters of the well know GM(1,1) and it is shown that this alternative procedure provides more reliable parameter estimates together with a simple visual framework for assessing whether the properties of the chosen GM(1,1) model are consistent with the actual data. In this paper a flexible generalisation of the Grey–Verhulst model is put forward which when applied to UK steel intensity of use produces very reliable multi step ahead predictions

Comparison of Selected Performance of Portfolio Investment Companies by Using of Grey Forecasting and Johnson’s Index in Tehran Stock Exchange Market

The purpose of resent research is to analysis and compares performance evaluation models of selected investment companies in Tehran Stock Exchange Market in the field of their portfolio management. The duration of research was between years 2009-2014. Statistical society the research is consisting of all active investment companies in in Tehran Stock Exchange Market which were 30 companies. Volume of research sample is by using of omit systematic method and also is by considering time of accepting in stock consisting of 14 companies. Data of research which are done based on compare couple and also gathered by financial ratio. Analysis process technic is used for compare couple analysis and used criteria weight determine in ash analysis. For determining company's priority based on financial ratio and weights of any of these companies; grey analysis is used. In present research all of the relations are approved by gain results. The result shows that there is no significant difference between obtained rankings by using of grey Forecasting Johnson ranking; it could be claim that there is no priority between grey forecasting and Johnson ranking. Results based on ranking of tested companies showed that criteria that used in this research were in same direction with liquidity criteria, so it is a confirmation of the fact that economic and accounting criteria could be a good and appropriate base for investors in selecting portfolio; and also that used criteria in the research is very powerful criteria for companies’ performance assessment

A hybrid approach based on ANP and grey relational analysis for machine selection

U proizvodnom sustavu, neodgovarajući izbor stroja može stvoriti mnoge probleme jer može negativno utjecati na proizvodnost, preciznost, fleksibilnost i kvalitetu proizvoda, pa se odabir stroja smatra važnim za učinkovitost sustava. Odlučivanje na temelju višestrukih kriterija - MCDM (Multi-Criteria Decision Making) koje se zasniva na različitim kriterijima i alternativama znači izabrati najpogodniji stroj između mnogih alternativa. U ovom radu o problemu izbora stroja, predlaže se hibridni pristup koji kombinira analitički mrežni postupak – ANP (Analytic Network Process) i postupak sive relacijske analize – GRA (Grey Relational Analysis). Za prepoznavanje težina izbornih kriterija i analizu problema izbora stroja primijenjen je ANP dok je GRA primijenjen za rangiranje. Svatko s osnovnim poznavanjem Microsoft Excel-a (tehničko osoblje, menađeri, proizvođač, prodavač itd.) može lako primijeniti predloženi pristup. Taj je pristup primijenjen kod problema izbora strojeva sa CNC usmjernikom koje bi kupila internacionalna kompanija. Kao rezultat, kompanija je razmotrila tu metodu i ishode prihvatljive i odgovarajuće za primjenu kod donošenja odluke o izboru stroja.In a manufacturing system, inappropriate machine selection may lead to many problems by negatively affecting productivity, precision, flexibility and product quality, and machine selection is considered to be an important subject to make the system effective. A Multi-Criteria Decision Making (MCDM) which is relying on the different criteria and alternatives is to choose the most suitable machine among many alternatives. In this study, for machine selection problem, a hybrid approach is proposed which combines Analytic Network Process (ANP) and Grey Relational Analysis (GRA). To identify weights of the selection criteria and to analyze the machine selection problem, the ANP is used whilst the GRA is used for ranking. The proposed approach can be applied easily by anybody (technical staff, managers, manufacturer, vendor, etc.) familiar with basic Microsoft Excel knowledge. The proposed approach is used for the selection problem of CNC router machines to be bought by an international company. As a result, the company has considered the method and outcomes acceptable and appropriate to implement to the machine selection decisions

Fuzzy time series analysis and prediction using swarm optimized hybrid model.

Time series forecasting has an extensive trajectory record in the fields of business, economics, energy, population dynamics, tourism, etc. where factor models, neural network models, Bayesian models are exceedingly applied for effective prediction. It has been exemplified in numerous forecasting surveys that finding an individual forecasting model to achieve the best performances for all potential situations is inadequate. Moreover, modern research endeavour has focused on a deeper understanding of the grounds. Rather than aim for designing a single superior model, it focused on the forecasting methods that are effective under certain situations. For instance, due to the qualitative nature of forecasting, a business can come up with diverse scenarios depending on the interpretation of data. Therefore, the organizations never rely on any individual forecasting model solely, rather focused on sets of individual models to attain the best possible knowledge of the future. The time series forecasting model has a great impact in terms of prediction. Many forecasting models related to fuzzy time series were proposed in the past decades. These models were widely applied to various problem domains, especially in dealing with forecasting problems where historical data are linguistic values. A hybrid forecasting method can be effective to improve forecast accuracy by merging sets of the individual forecasting models. Numerous hybrid forecasting models have been proposed last couple of years that combined fuzzy time series with the evolutionary algorithms, but the performance of the models is not quite satisfactory. In this research, a novel hybrid fuzzy time series forecasting model is proposed that used the historical data as the universe of discourse and the automatic clustering algorithm to cluster the universe of discourse by adjusting the clusters into intervals. Furthermore, the particle swarm optimization algorithm is also examined to improve forecasted accuracy. The proposed method is considered to forecast student enrolment of the University of Alabama. The model achieves a significant improvement in forecast accuracy as compared to state-of-the-art hybrid fuzzy time series forecasting models. It is obvious from the literature that no forecasting technique is appropriate for all situations. There is substantial evidence to demonstrate that combining individual forecasts produces gains in forecasting accuracy. The addition of quantitative forecasts to qualitative forecasts may reduce forecast accuracy. Individual forecasts are combined based on either the simple arithmetic average method or an artificial neural network. Research has not yet revealed the conditions for the optimal forecast combinations. This thesis provides a few contributions to enhance the existing combination model. A set of Individual forecasting models is used to form a novel combination forecasting model based on the characteristics of resulting forecasts. All methods derived in this thesis are thoroughly tested on several standard datasets. The related characteristics of the resulting forecasts are observed to have different error decompositions both for hybrid and combination forecasting model. Advanced combination structures are investigated to take advantage of the knowledge of the forecast generation processes

Mixture of Poisson distributions to model discrete stock price changes.

An application of a mixture of Poisson distributions is proposed to model the discrete changes in stock price based on the minimum price movement known as `tick-size\u27. The parameters are estimated using the Expectation-Maximization (EM) algorithm with a constant mixing probability as well as mixing probabilities which depend on order size. The model is evaluated using simulations and real data. Both the simulated and real data show reasonable estimates. Several adjustments are made to the model implementation to improve the efficiency with user written codes for the Newton Raphson algorithm and also implementing one of the most recent versions of the EM algorithm (PEM). Both the improvements show an exponentially increasing efficiency to the implementation. Further a Clustered Signed model is proposed to use summarized data to reduce the amount of data to be used in the model implementation using the discrete order sizes and the signs of the discrete stock price changes. The clustered model provided a significant time efficiency. A parametric bootstrap procedure is also considered to assess the significance of the order size on the mixing probabilities. The results show that the use of a variable mixture probability, which depends on the order size, is more appropriate for the model. The methods are illustrated with data from simulations and real data from Federal Express

Gene expression programming for Efficient Time-series Financial Forecasting

Stock market prediction is of immense interest to trading companies and buyers due to high profit margins. The majority of successful buying or selling activities occur close to stock price turning trends. This makes the prediction of stock indices and analysis a crucial factor in the determination that whether the stocks will increase or decrease the next day. Additionally, precise prediction of the measure of increase or decrease of stock prices also plays an important role in buying/selling activities. This research presents two core aspects of stock-market prediction. Firstly, it presents a Networkbased Fuzzy Inference System (ANFIS) methodology to integrate the capabilities of neural networks with that of fuzzy logic. A specialised extension to this technique is known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The research presented in this thesis aims at the modelling and prediction of short-tomedium term stock value fluctuations in the market via genetically tuned stock market parameters. The technique uses hierarchically defined GP and gene-expressionprogramming (GEP) techniques to tune algebraic functions representing the fittest equation for stock market activities. The technology achieves novelty by proposing a fractional adaptive mutation rate Elitism (GEP-FAMR) technique to initiate a balance between varied mutation rates between varied-fitness chromosomes thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against five stock market companies with each having its own trading circumstances during the past 20+ years. The proposed GEP/GP methodologies were evaluated based on variable window/population sizes, selection methods, and Elitism, Rank and Roulette selection methods. The Elitism-based approach showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods. The contribution of this research to theory is that it presented a novel evolutionary methodology with modified selection operators for the prediction of stock exchange data via Gene expression programming. The methodology dynamically adapts the mutation rate of different fitness groups in each generation to ensure a diversification II balance between high and low fitness solutions. The GEP-FAMR approach was preferred to Neural and Fuzzy approaches because it can address well-reported problems of over-fitting, algorithmic black-boxing, and data-snooping issues via GP and GEP algorithmsSaudi Cultural Burea