A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized.In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs).     

A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock

Linear model is a general forecasting model and moving average technical index (MATI) is one of useful forecasting methods to predict the future stock prices in stock markets. Therefore, individual investors, stock fund managers, and financial analysts attempt to predict price fluctuation in stock markets by either linear model or MATI. From literatures, three major drawbacks are found in many existing forecasting models. First, forecasting rules mined from some AI algorithms, such as neural networks, could be very difficult to understand. Second, statistic assumptions about variables are required for time series to generate forecasting models, which are not easily understandable by stock investors. Third, stock market investors usually make short-term decisions based on recent price fluctuations, i.e., the last one or two periods, but most time series models use only the last period of stock price. In order to overcome these drawbacks, this study proposes a hybrid forecasting model using linear model and MATI to predict stock price trends with the following four steps: (1) test the lag period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and calculate the last n-period moving average; (2) use subtractive clustering to partition technical indicator values into linguistic values based on data discretization method objectively; (3) employ fuzzy inference system (FIS) to build linguistic rules from the linguistic technical indicator dataset, and optimize the FIS parameters by adaptive network; and (4) refine the proposed model by adaptive expectation models. The proposed model is then verified by root mean squared error (RMSE), and a ten-year period of TAIEX is selected as experiment datasets. The results show that the proposed model is superior to the other forecasting models, namely Chen's model and Yu's model in terms of RMSE.     

Simple atmospheric dispersion model to estimate hourly ground-level nitrogen dioxide and ozone concentrations at urban scale

Atmospheric nitrogen dioxide (NO₂) and ozone (O₃) present potential health risk at large urban centres worldwide. Modelling their ground-level concentrations is a fundamental part of urban air quality assessment studies. Simple atmospheric dispersion models are particularly useful in places lacking detailed input data to run complex models and for applications requiring a large number of simulations, also allowing high spatial and temporal resolution even for long-term calculations. The DAUMOD-GRS urban atmospheric dispersion model has been developed aiming to have these features. This work presents its performance evaluation considering hourly concentrations of NO₂ and O₃ measured at twenty sites across the Metropolitan Area of Buenos Aires (MABA), Argentina. Results show an acceptable model performance, with a small tendency to underestimate NO₂ and to overestimate O₃. By grouping the monitoring sites in regions having different emission conditions, it is found that the model reproduces well the observed urban-suburban concentration gradients.     

A Bayesian approach to forecasting daily air-pollutant levels

Forecasting air-pollutant levels is an important issue, due to their adverse effects on public health, and often a legislative necessity. The advantage of Bayesian methods is their ability to provide density predictions which can easily be transformed into ordinal or binary predictions given a set of thresholds. We develop a Bayesian approach to forecasting PM\(_{10}\) and O\(_3\) levels that efficiently deals with extensive amounts of input parameters, and test whether it outperforms classical models and experts. The new approach is used to fit models for PM\(_{10}\) and O\(_3\) level forecasting that can be used in daily practice. We also introduce a novel approach for comparing models to experts based on estimated cost matrices. The results for diverse air quality monitoring sites across Slovenia show that Bayesian models outperform classical models in both PM\(_{10}\) and O\(_3\) predictions. The proposed models perform better than experts in PM\(_{10}\) and are on par with experts in O\(_3\) predictions—where experts already base their predictions on predictions from a statistical model. A Bayesian approach—especially using Gaussian processes—offers several advantages: superior performance, robustness to overfitting, more information, and the ability to efficiently adapt to different cost matrices.     

An evolutionary system for ozone concentration forecasting

Nowadays, with more than 50 % of the world’s population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems.     

Forecasting hourly $${\hbox {NO}_{2}}$$ concentrations by ensembling neural networks and mesoscale models

In the framework of extreme pollution concentrations being more and more frequent in many cities nowadays, air quality forecasting is crucial to protect public health through the anticipation of unpopular measures like traffic restrictions. In this work, we develop the core of a 48 h ahead forecasting system which is being deployed for the city of Madrid. To this end, we investigate the predictive power of a set of neural network models, including several families of deep networks, applied to the task of predicting nitrogen dioxide concentrations in an urban environment. Careful feature engineering on a set of related magnitudes as meteorology and traffic has proven useful, and we have coupled these neural models with mesoscale numerical pollution forecasts, which improve precision by up to 10%. The experiments show that some neural networks and ensembles consistently outperform the reference models, particularly improving the Naive model’s results from around (20%) up to (57%) for longer forecasting horizons. However, results also reveal that deeper networks are not particularly better than shallow ones in this setting.

     

Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

This work deals specifically with the use of a neural network for ozone modelling in the lower atmosphere. The development of a neural network model is presented to predict the tropospheric (surface or ground) ozone concentrations as a function of meteorological conditions and various air quality parameters. The development of the model was based on the realization that the prediction of ozone from a theoretical basis (i.e. detailed atmospheric diffusion model) is difficult. In contrast, neural networks are useful for modelling because of their ability to be trained using historical data and because of their capability for modelling highly non-linear relationships. The network was trained using summer meteorological and air quality data when the ozone concentrations are the highest. The data were collected from an urban atmosphere. The site was selected to represent a typical residential area with high traffic influences. Three neural network models were developed. The main emphasis of the first model has been placed on studying the factors that control the ozone concentrations during a 24-hour period (daylight and night hours were included). The second model was developed to study the factors that regulate the ozone concentrations during daylight hours at which higher concentrations of ozone were recorded. The third model was developed to predict daily maximum ozone levels. The predictions of the models were found to be consistent with observations. A partitioning method of the connection weights of the network was used to study the relative percent contribution of each of the input variables. The contribution of meteorology on the ozone concentration variation was found to fall within the range 33.15–40.64%. It was also found that nitrogen oxide, sulfur dioxide, relative humidity, non-methane hydrocarbon and nitrogen dioxide have the most effect on the predicted ozone concentrations. In addition, temperature played an important role while solar radiation had a lower effect than expected. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modelling.     

An operational real-time air quality modelling system for industrial plants

The advances on computer power capabilities and air quality modelling systems (AQMS) has reached to a high degree of sophistication during the last decade. Nowadays, the state-of-the-art air quality modelling systems such as MM5–CMAQ can handle the evaluation of air pollution concentrations in a very high detail in time and space. MM5 is a non-hydrostatic modelling system developed by PSU/NCAR (USA) during the last 20 years and continuously updated by the scientific community. CMAQ is a Community Multi-scale Air Quality Modelling System for simulating the transport and chemical concentrations in the air in 3D space and time developed by EPA (USA) in 2000. Both systems are complementary and allow a full simulation of the atmospheric flow to determine the amount of air pollution concentration exists in the 3D space and during a specific period of time. TEAP (a tool to evaluate the air quality impact of industrial plants) is an EUREKA project coordinated by UPM with the participation of INDRA S.A., Institute of Physics (Lithuania) and AB ‘MAZEIKIU NAFTA’ (Lithuania). This tool allows the industrial plants – and electric power plants – to have a full control in real-time and forecasting mode of the impact of the industrial emission under daily basis by using the so-called ON–OFF operational mode. The ON–OFF mode requires to run the AQMS both time in parallel by using the industrial plant emissions (ON) and excluding them (OFF). The system allows a full knowledge of the impact of industrial emissions for the next 48–72 h in full space (3D) domain, time and for every criteria pollutant (NO_x, SO₂, CO, O₃, PM). The system requires a powerful post-processing analysis module and a clustering approach to optimize the computer capabilities. The system is mounted over a PC cluster platform under Linux operating system. Results show a full and detail analysis of the amount of air pollutant concentrations due to the industrial plant emissions in time and space and under daily operational basis. The EU Air Quality Directives mark the air concentration limits to be taken into account into the TEAP system in forecasting mode to be fulfilled. The system allows to simulate also different industrial emission reduction strategies according to the optimal economical/production balance. The system can easily be adapted for emergency use.     

A modeling of the sea breeze and its impacts on ozone distribution in northern Taiwan

A high-resolution air pollution numerical model system (APOPS) is applied to simulate the sea/land breeze and its impacts on the ozone distribution in northern Taiwan. The system can successfully simulate local flow patterns such as sea/land breezes and mountain-valley wind. The predicted surface ozone concentrations also agree with observed surface ozone values (Wang, Z., et al., Tellus, 52B, 2000, 1189). The sea/land breezes in northern Taiwan play a significant role in the distribution of ozone and transport of ozone from the urban to the coastal and mountain areas. The sea breeze is a weak system, extending vertically to a height of less than 1 km with the wind speed less than 4 ms^-1. The land breeze can transport the photochemically produced ozone and its precursors over the sea. The accumulated ozone on the sea can return to the land in the daytime with the sea breeze. This kind of transport tends to contribute significantly to high-ozone episodes in clean coastal and mountain regions.     

Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong

The objective of this study was to apply preprocessing and ensemble artificial intelligence classifiers to forecast daily maximum ozone threshold exceedances in the Hong Kong area. Preprocessing methods, including over-sampling, under-sampling, and the synthetic minority over-sampling technique, were employed to address the imbalance data problem. Ensemble algorithms are proposed to improve the classifier's accuracy. Moreover, a distance-based regional data set was generated to capture ozone transportation characteristics. The results show that a combination of preprocessing methods and ensemble algorithms can effectively forecast ozone threshold exceedances. Furthermore, this study advises on the relative importance of the different variables for ozone pollution prediction and confirms that regional data facilitate better forecasting. The results of this research can be promoted by the Hong Kong authorities for improving the existing forecasting tools. Moreover, the results can facilitate researchers' selection of the appropriate techniques in their future research.     

An investigation of thresholds in air pollution–mortality effects

In this paper we introduce and implement new techniques to investigate threshold effects in air pollution–mortality relationships. Our key interest is in measuring the dose–response relationship above and below a given threshold level where we allow for a large number of potential explanatory variables to trigger the threshold effect. This is in contrast to existing approaches that usually focus on a single threshold trigger. We allow for a myriad of threshold effects within a Bayesian statistical framework that accounts for model uncertainty (i.e. uncertainty about which threshold trigger and explanatory variables are appropriate). We apply these techniques in an empirical exercise using daily data from Toronto for 1992–1997. We investigate the existence and nature of threshold effects in the relationship between mortality and ozone (O₃), total particulate matter (PM) and an index of other conventionally occurring air pollutants. In general, we find the effects of the pollutants we consider on mortality to be statistically indistinguishable from zero with no evidence of thresholds. The one exception is ozone, for which results present an ambiguous picture. Ozone has no significant effect on mortality when we exclude threshold effects from the analysis. Allowing for thresholds we find a positive and significant effect for this pollutant when the threshold trigger is the average change in ozone two days ago. However, this significant effect is not observed after controlling for PM.     

An overview of air quality analysis by big data techniques: Monitoring,forecasting, and traceability

With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas.     

Deriving high-resolution urban air pollution maps using mobile sensor nodes

Up-to-date information on urban air pollution is of great importance for environmental protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 50 million measurements. We collected the measurements throughout more than two years using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100 m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with sub-weekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error–a standard metric to evaluate the accuracy of air quality models–of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.     

A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle, 1982, Cheng et al., 2010. After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), produce complex and unintelligible rules; and (2) statistic forecasting models, such as time series, require some basic assumptions for variables and build forecasting models based on mathematic equations, which are not easily understandable by stock investors. In order to refine these drawbacks of past models, this paper has proposed a model, based on adaptive-network-based fuzzy inference system which uses multi-technical indicators, to predict stock price trends. Three refined processes have proposed in the hybrid model for forecasting: (1) select essential technical indicators from popular indicators by a correlation matrix; (2) use the subtractive clustering method to partition technical indicator value into linguistic values based on an data discretization method; (3) employ a fuzzy inference system (FIS) to extract rules of linguistic terms from the dataset of the technical indicators, and optimize the FIS parameters based on an adaptive network to produce forecasts. A six-year period of the TAIEX is employed as experimental database to evaluate the proposed model with a performance indicator, root mean squared error (RMSE). The experimental results have shown that the proposed model is superior to two listing models (Chen’s and Yu’s models).     

Forecasting of the daily meteorological pollution using wavelets and support vector machine

The paper presents the method of daily air pollution forecasting by using support vector machine (SVM) and wavelet decomposition. Based on the observed data of NO₂, CO, SO₂ and dust, for the past years and actual meteorological parameters, like wind, temperature, humidity and pressure, we propose the forecasting approach, applying the neural network of SVM type, working in the regression mode. To obtain the acceptable accuracy of forecast we decompose the measured time series data into wavelet representation and predict the wavelet coefficients. On the basis of these predicted values the final forecasting is prepared. The paper presents the results of numerical experiments on the basis of the measurements made by the meteorological stations, situated in the northern region of Poland.     

A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight

Effective forecasting of the air pollutant concentration is crucial for a robust air quality early-warning system and has both theoretical and practical significance. However, the accidental and cognitive uncertainty in the model selection or parameter setting of a single system will result in inaccurate and unstable forecasting results. Thus, in this paper, a novel fuzzy combination forecasting system based on the data preprocessing, fuzzy theory, and advanced optimization algorithm is proposed to improve the accuracy and stability of forecasting results. Based on the fuzzy theory and decorrelation maximization method, our proposed forecasting system can considering more information and maintaining the diversity of models. Moreover, Cuckoo Search algorithm applied in the system can determine the optimal weights for models aggregation. Several experiments based on PM${}_{\mathrm{2.5}}$ and PM₁₀ datasets in three cities are analyzed and discussed to verify the excellent performance of our proposed forecasting system, and the results indicate that the forecasting system outperforms others with respect to the accuracy, stability and generalization capabilities which are the basis of a robust air quality early-warning system in practice.     

Modelling the Spatio-Temporal Distribution of Ambient Nitrogen Dioxide and Investigating the Effects of Public Transit Policies on Population Exposure

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NO_x) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO₂) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO₂ concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO₂ hot spots. This is associated with a reduction in total NO_x emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO₂ concentration across the study domain and the mean NO₂ exposure across the sample population results from the fact that NO₂ concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains.     

Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics

Due to the worldwide trend of industrialization and urbanization, air pollutants were emitted heavily on a global scale particularly in developing countries, which produces adverse effects on human health by causing health problems such as respiratory and lung diseases. Many regression models based on land use types and urban fabrics have been built to analyze the spatiotemporal distribution of air pollutants, however, few of them examined the relationship between urban morphological characteristics and the distribution of air pollutants in a megacity. This study investigates such relationships for six types of air pollutants (PM2.5, PM10, SO₂, NO₂, O₃, and CO) and a composite AQI (Air Quality Index) based on hourly data at 35 monitoring stations in Beijing in 2016, with morphological characteristics (Morphological building index), meteorological factors (Land Surface Temperature, LST), land use (vegetation, road length, gas station and industry point data), and population distribution data. We also analyzed the results with spatiotemporal regression and SSH (Spatial Stratified Heterogeneity) models respectively. According to the spatiotemporal regression model, the morphological building index (MBI) shows a strong correlation with the dispersion of PM2.5 (R² = 0.81) and AQI (R² = 0.80) in the warm season and this finding was reinforced through the Leave-one-out-cross-validation (LOOCV) analysis. From the SSH analysis, the road length in a large proximal region impacts air pollutants the most, especially for O₃; and population density significantly affects PM 2.5, AQI, SO₂, and NO₂ in the cold season. From an integrated interpretation, distance to nearest industry impacts the spatial distribution of NO₂ in cold season, while it impacts that of PM2.5 and AQI in both warm and cold seasons. The research finds that these two models supplement each other well and together help to give us a better understanding of how air quality is affected in the urban landscape.     

An optimization platform for Poland's power sector considering air pollution and health effects

The article demonstrates the features and applicability of the πESA platform designed for optimization of the Poland's power sector considering air pollution and health effects. πESA is comprised of: a bottom-up energy-economic model TIMES-PL, an air quality modelling system Polyphemus and a module for assessment of environmental and health impacts MAEH. It has been designed as a web application employing computational resources of the ZEUS cluster of the PL-Grid infrastructure. The results show, that the impact of carbon prices on the fuel and technological power generation structure is much stronger as compared to impact of fuel prices. Future PM emissions from the centralized power and heat generation sector do not differ much irrespective of energy scenario considered. For analysed cases, the statistical life expectancy in Poland due to long-term exposure to PM_2.5 air pollution is reduced on average by approx. 183 days. That gives over 12 million years lost for all cohorts included in the analysis.