Forecasting and Prediction Tools

This is the RAW DATA for a neural network price prediction system to be utilized for the prediction of stock prices on US markets.  To date the performances are doing well.

I have experiemented with multiple architectures and now am attempting a 4 day swing trade prediction network that has its results included here.
so far:

Neural net 1= average portfolio error .4627%
Neural net 2= average portfolio error 1.7%
and
4 day swing trade neural net = average portfolio error of 4.84%

There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is to investigate if using these indicators as the input variables to a neural network will provide more accurate stock trend predictions, and whether they will yield higher trading pro¢ts than the traditional technical indicators. Three neural networks are examined in the study to predict the short-term trend signals of three stocks across different market industries. The overall results indicate that the proportion of correct predictions and the pro¢tability of stock trading guided by these neural networks are higher than those guided by their benchmarks.

Background/objectives: When performing activity associated with walking, the amount of walking a person does often will depend on their plans. This study was designed to evaluate the relationship between motor planning and ambulatory persistence in participants with Parkinson's disease (PD) and to see if ambulatory persistence was related to the ability to perform activities of daily living (ADL). Methods: 20 individuals with idiopathic PD were recruited to perform the Trail making Test (a test of motor planning) and to wear a step activity monitor for 48 h. The measurement of persistence of an ambulatory event consisted of the number of steps taken during an event and an ambulatory event was defined as continuous ambulation (taking step) without pausing for 3 or more seconds. The resumption of taking step (ambulation) after 3 or more seconds counted as a new ambulatory event. UPDRS-motor and ADL scale were also obtained. Analysis and results: The cumulative percentage of the total ambulatory events at each number of steps was plotted for each subject which when plotted could be described as a sigmoid curve. We found that this sigmoidal curve defined by the equation y = xn/(kn + xn), fit the data well, where k represents a constant specific to each subject, x represents the number of steps during each ambulatory event, and y represents the projected percentage of movement events containing x number of steps or less. (Root Mean Square Error (RMSE) = 0.02, R2 = 0.98). Trail making test part A was highly associated with the constant k (R = −0.74, p < 0.001). The constant k was also highly associated with the UPDRS ADL subscale (R = −0.81, p = 0.0001). A forward bivariate regression model including Part A of the Trail making test, and the UPDRS-ADL subscale predicted 66% of the variability of the constant k. The overall number of steps taken per day, and the UPDRS motor subscale did not contribute to the model. Conclusions: Defective motor planning in Parkinson's disease as measured by poor performance on a Trail making test is associated with a measurable alteration in ambulatory persistence, and altered ambulatory persistence, quantified by our proposed model parameter, correlates highly with the UPDRS ADL score. Thus, cognitive-motor planning defects might be a major source of disability in PD. We suggest that in future clinical practice gait tests can be used in order to quantify short-term planning ability in neurodegenerative diseases.

This paper will review the current state of both biological and non-biological nanotechnology and will predict the near future of this technology using physical and economic forecasting methods. Nanotechnology is defined as the precise manipulation of matter under 1 micrometer.

This paper explores the range of likely and potential progress on poverty eradication in fragile states to 2030. Using the International Futures model and recently released 2011 International Comparison Program data, this paper calculates current (2015) poverty for a US$1.90 poverty line, and subsequently runs three scenarios. The estimates suggest that there are 485 million poor in fragile states in 2015, a 33.5 per cent poverty rate. This paper's Base Case scenario results in a forecasted 22.8 per cent poverty rate in fragile states by 2030. The most optimistic scenario yields a 13.1 per cent poverty rate for this group of countries (257 million). An optimistic scenario reflecting political constraints in fragile states yields a 19.1 per cent poverty rate (376 million). Even under the most optimistic circumstances, fragile states will almost certainly be home to hundreds of millions of poor in 2030, suggesting that the world must do things dramatically differently if we are to reach the high hanging fruit and truly 'leave no one behind' in the next fifteen years of development.

INTRODUCTION This paper is an exploration of the science of prediction. It examines what we are good and bad at predicting and why; what influences our ability to make reliable predictions and what the differences arise in the data when we ask questions predictively. It provides advice on the best ways to ask predictive questions and the best techniques for conducting predictive research. It is based on the learnings from over 30 research on research experiments conducted over a three year period, the meta analysis of over 500 predictions and a review of the prominent academic literature published on this topic. We hope this will act as a guide for the market research industry wishing to exploit prediction protocols in their research.

In 2013, I proposed using drones to extinguish future forest fires. Admittedly, both the idea and the technology were crude, and proposing an idea like this is not the same as implementing a solution. During the first few minutes, between the time when a fire first starts and when it reaches a point of being out of control, is a containment window where only a few gallons of water or a few pounds of fire retardant is necessary to put the evil genie back into its bottle. Using a fleet of surveillance drones, equipped with special infrared cameras, fires can be spotted during the earliest moments of a containment window, signaling a fleet of extinguisher drones to douse the blaze before anything serious happens. Drones specifically designed for extinguishing forest fires have the potential to eliminate virtually 100% of the devastating fires that dominate newspaper headlines every summer. But is that what we want?

Danske virksomheder står i disse år overfor at tilpasse sig til den fjerde industrielle revolution, som bygger på den teknologiske udvikling og ikke mindst digitalisering (Erhvervs & Vækstministeriet, 2016). Kollektiv intelligens er i høj grad et begreb, der er ved at udbredes i en digitaliseret samtid. Nyere forskningslitteratur påviser, at ”crowds” har kollektiv intelligens, der kan bidrage med nye kreative og innovative løsninger, og endda forudsige fremtiden for virksomheder ganske præcist (Cowgill & Zitzewitz, 2015; Dahan, Soukhoroukova, & Spann, 2010; Hong & Page, 2001; 2004; Surowiecki, 2004).
Formålet med denne forskningsrapport er at skabe et overblik vedrørende viden om og anvendelsen af kollektiv intelligens og relaterede crowdsourcing-metoder blandt beslutningstagere i Danmarks ledende virksomheder på tværs af sektorer.
Empirien er indsamlet ved semi-strukturerede spørgeskemaundersøgelser i form af telefoninterviews med CEOs, direktører, afdelingsledere og mellemledere i 50 af de største virksomheder i Danmark på tværs af sektorer og inkluderer i alt 69 besvarelser.
Som det første større kvantitative og kvalitative studie i Danmark af kollektiv intelligens adfærd blandt danske virksomheder, undersøger vi virksomhedernes brug af crowdsourcing-metoder, herunder crowdsourcing af kreative og innovative løsninger samt anvendelsen af prediction markets og brugen af crowd forudsigelser uden markeder.
Hovedkonklusionerne af forskningsrapporten er, at de største danske virksomheder står overfor et anseligt potentiale med hensyn til at implementere kollektiv intelligens adfærd og crowdsourcing teknikker for at skabe øget konkurrencefordele i fremtiden. Virksomheder, der har brugt crowdsourcing tidligere har i højere grad forventning om at bruge crowdsourcing end virksomheder, der ikke har brugt crowdsourcing. Virksomheder, der scorer højt på industri-ekspertise har større forventning om at bruge crowdsourcing i fremtiden. Eksternt pres på virksomheder giver samtidig en større sandsynlighed for crowdsourcing i fremtiden. Det er særligt i sektorer, der har en stigende mangfoldighed i kunders ønsker, at man forventer at anvende crowdsourcing.
Til sammenligning med anerkendte globale virksomheder, kan det konkluderes at danske erhvervsledere, som er repræsenteret i udvalget har viden om kollektiv intelligens og crowdsourcing, mens anvendelsen af disse områder er mindre udbredt. Det betyder at virksomhederne har et stort uudnyttet potentiale med henblik på at tilpasse sig den globale udvikling indenfor anvendelsen af kollektiv intelligens og crowdsourcing-metoder.

This article uses methods for predicting plant performance indicators in Kazakhstan. In the work, using deep learning, visualization of predicted indicators (indicators and others), statistics from predicted values and identified changes, time series have been developed. Sentinel satellite data and statistical indicators for the last few years for the agricultural territories of Kazakhstan are used as primary data. It is found that the upward trend in wheat quality, however, increases the size of fertilizers, variables based on the NDVI also significantly contribute to the forecasting model. It has been shown that the amount of applied fertilizer has stabilized in the past few years due to economic and environmental constraints, so NDVI-based models will become increasingly important for enhancing forecasting models. Four machine learning algorithms have been evaluated and compared, namely boosted regression trees (BRT) and support vector machine (SVM), to map and predict the field yield of the Experimental Oil Farm in East Kazakhstan using readily available additional data. Based on the results of the work, a forecast of crop yields and general statistical recommendations for increasing yields were obtained.

The forecasting of electricity consumption and demand plays a pivotal role in electric power systems planning. This paper proposes the combination of forecasts from two approaches with the aim of improving the forecasting accuracy, in order to make the best use of the installed transmission and generating capacity. In the first approach, the consumption time series is decomposed by wavelet analysis and a Box-Jenkins model is fitted to each wavelet component, following which the individual components forecasts are added to compute the total consumption forecast. The alternative approach, uses the Singular Spectrum Analysis technique to model the consumption time series in order to shrink the noise level. Thereafter, the Box-Jenkins model is used to forecast the filtered time series, producing a second forecast for the consumption series. Eventually, the two forecasts are combined geometrically in order to minimize the mean square error. The proposed methodology is illustrated by a computational experiment with the time series of residential consumption of electricity in Brazil.

Mercu Buana University Campus D is part of Mercu Buana University which began the operational in 2013. Since 2013 until 2017, Mercu Buana University Campus D still got less than a target about getting the new student.This can be due to various things including the lack of precisely marketing strategy undertaken.Therefore, in this study the authors make an application by implementing the concept of data mining using clustering and forecasting methods to obtain information from existing data registrants. So, the information can be used by decision makers to determine effective and efficient marketing strategies.

Bottled Drinking Water (AMDK) is processed water, without other food ingredients and food additives, packaged, and safe to drink. AMDK itself which is widely known in the community is national product AMDK. However, local products have emerged to try to compete with national product AMDK. As for local product AMDK which is produced mainly in Madura, the average product is made from Islamic boarding schools. From the level of competition, the idea is emerged for forecasting primarily in the field of sales using the fuzzy Sugeno method. The results obtained from fuzzy Sugeno are turned out to produce a MAPE that the value for local AMDK is 6.4795 and a national AMDK is 2.5353, so that the conclusion about national production is closer to the real value than local AMDK. This study contributes that the predictive value of sales competition is quite accurate based on the MAPE value is below 10. This study provides recommendations to local sellers to make improvements in factors in order ...

This paper presents the study of Support Vector Regression (SVR) to forecast the future streamflow discharge
using past streamflow and rainfall data, which is closely related to regularization network and Gaussian
processes. A Gaussian Radial Basis Function (RBF) kernel framework was built on the dataset to optimize the
tuning parameters and to obtain the moderated output. It has been observed from various studies that the
prediction ability of RBF kernel function is better for regression problems. The training process of Support
Vector Machine (SVM) involves the selection of both kernel parameters and regularization constants. The
constants such as γ, ε, C and σ are the parameters of SVR which were optimized to obtain the desired outputs.
Where parameter C determines trade-off between model complexity and degree to which deviations are larger
than ε are tolerated in optimization formulation, and the parameter ε controls the width of ε –sensitive zone. The
study area include the upstream part of Jayakwadi dam. The prediction is based on flow at two Gauge and
Discharge Sites (GDS) i.e. Kopargaon and Nagamthan and rainfall data at three Standard Rain gauge Stations
(SRG) i.e. Rahata, Khirdisathe, and Wadala Mahadev in Maharashtra, India. The daily data values of these
stations for 18 years were collected from Hydrological Data User Group (HDUG), Nashik. About 70% of data
were used for training purpose and remaining 30% of data were used for testing purpose. The experimental
results showed that the SVR algorithm is reliable and efficient method for streamflow prediction, which has an
important impact on the water resources management of region.
Keywords: Support Vector Machine (SVM), Support Vector Regression (SVR), Radial Basis Function (RBF)
Kernel, ε-sensitive zone, regularization parameters.

In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of CO2 pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be developed to reduce energy waste (i.e. policies for Demand Response and Demand Side Management). This paper discusses an innovative methodology for smart building indoor air-temperature forecasting. This methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of six years indoor air-temperature values of a building demonstrator. In detail, we have studied three characterizing rooms and the whole building. Experimental results of energy prediction are presented and discussed.

This study examines and analyses the use of a new recurrent neural network model: Jordan Pi-Sigma Network (JPSN) as a forecasting tool. JPSN&#x27;s ability to predict future trends of temperature was tested and compared to that of Multilayer Perceptron (MLP) and the standard Pi-Sigma Neural Network (PSNN); trained with the standard gradient descent algorithm. A set of historical temperature measurement for five years from Malaysian Meteorological Department was used as input data to train the networks for the next-day ...

The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.

Errors in estimating and forecasting often result from the failure to collect and consider enough relevant information. We examine whether attributes associated with persistence in information acquisition can predict performance in an estimation task. We focus on actively open-minded thinking (AOT), need for cognition, grit, and the tendency to maximize or satisfice when making decisions. In three studies, participants made estimates and predictions of uncertain quantities, with varying levels of control over the amount of information they could collect before estimating. Only AOT predicted performance. This relationship was mediated by information acquisition: AOT predicted the tendency to collect information, and information acquisition predicted performance. To the extent that available information is predictive of future outcomes, actively open-minded thinkers are more likely than others to make accurate forecasts.

This paper present an application of Artificial Neural Networks (ANNs) in the renewable energy domain and, more particularly, to predict solar energy. We look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. In previous studies, we have demonstrated that an optimized ANN with endogenous inputs can forecast the solar radiation on a horizontal surface with acceptable errors. Thus we propose to study the contribution of exogenous meteorological data to our optimized PMC and compare with different forecasting methods used previously: a naïve forecaster like persistence, an ANN with preprocessing using only endogenous inputs and an ANN with pre-processing using endogenous. Although intuitively the use of meteorological data may increase the quality of prediction, the obtained results are relatively mixed. The use of exogenous data generates a decrease of nRMSE between 0.5% and 1% for the two studied locations. The absolute error (RMSE) is decreased by 52 Wh/m²/day in the simple endogenous case and 335 Wh/m²/day for the persistence forecast.

Study 1: 630 spree-shooters [1936-2021] (1,650 deaths; 3,123 injuries; 194 suicides [31%]), 623 controls logistic regression differences (F= 260.44, df=10/1242, R=.82, R2 =.68, p<.01): (1) homicidal ideation; (2) planning-preparation; (3) stressful-life-event; (4) revenge-motive; (5) acquired-multiple-weapons; (6) elicited-concern; (7) school-location; (8) personal-grievance; (9) suicide; (10) current-student. Study 2: 15 spree-shooters differences, adult, teen: [SP] violence (F=17.48, 123.09); [MMPI-2/A] infrequency (F=92.15, 17.22); lie (F=13.13, 33.91); depression (F=37.76, 26.18); psychopathic-deviance (F=44.66, 57.45); paranoia (F=50.58, 23.92); schizophrenia (F=53.85, 21.69); alcohol (F=42.01, 16.84); addiction (F=57.34, 38.88) compared with 23 homicidal, 36 controls. Spree-shooter loss (1936-2021) = [$2,416,042,490 (630 @ $3,834,988.08) + $6,327,730,332 (1,650 @ $3,834,988.08) + $105,474,702.96 (3,123 @ $33,773.52) = $8,849,247,525.36] + [insurance, tax-increases $11,504,021,782.97 ($8,849,247,525.36 x 1.3] =$20,353,269,308.33. Projecting 2021 to 2105 insurance industry with no computer tests, machine learning equations, $40,706,538,616.66, 3,330 deaths, 6,246 injuries, 388 suicides. Projecting 2017 U.S. Church pedophilia loss (2012-2037, 2038-2056, 2057-2082, 2083-2107), $5,719,865,400 x 5 = $28,599,327,000, 5,679 x 5 = 28,395 victims,

In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average (SARIMA) model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.

Following a definition of the basic terms employed, the author examines
the interrelationships between planning, policy-making and forecasting.
The main trends in futures research are described, and some forecasting methods and techniques conventionally used in tourism studies are considered. Criteria are
derived by which tourism policy-makers could measure the usefulness

Key words: Future Studies; Tourism; Forecasting Methods
of forecasts presented to them.

This study presents an objective verification of different icing algorithms for two periods from 5 December 2012 to 17 April 2013 and from 9 October to 11 December 2013. The predicted icing potential and severity are evaluated against pilot reports of aircraft icing below 23 000 feet over Eastern United States. The icing algorithms are applied to the deterministic forecasts of European Centre for Medium-Range Weather Forecasts (ECMWF). Two of these algorithms are based only on forecasts of temperature (T) and relative humidity (RH), but the first uses fixed thresholds and the second uses a fuzzy logic approach. Although the second algorithm detects less icing events, it is more efficient with respect to the warning area, i.e. it forecasts icing over a smaller percentage of the airspace than the first algorithm. The impact of adding information from cloud liquid water (CLW) content and/or vertical velocity in the fuzzy logic scheme is assessed. Algorithms using T, CLW and vertical velocity as predictors are also tested. Most of the algorithms reveal a good performance in discriminating between icing and no-icing conditions, but are less skilful in forecasting the icing severity. The results also show that the use of CLW is essential to avoid issuing too many warnings. Moreover, the algorithm combining T, RH, CLW and vertical velocity attains the best performance. This algorithm, SFIP O , has been operational at the Portuguese Meteorological Watch Office since June 2013, and an example of a severe icing episode in February 2014 over mainland Portugal is presented. KEY WORDS aircraft icing; ECMWF forecast; fuzzy logic algorithm; cloud liquid water; PIREP

Errors in estimating and forecasting often result from the failure to collect and consider enough relevant information. We examine whether attributes associated with persistence in information acquisition can predict performance in an estimation task. We focus on actively open-minded thinking (AOT), need for cognition, grit, and the tendency to maximize or satisfice when making decisions. In three studies, participants made estimates and predictions of uncertain quantities, with varying levels of control over the amount of information they could collect before estimating. Only AOT predicted performance. This relationship was mediated by information acquisition: AOT predicted the tendency to collect information, and information acquisition predicted performance. To the extent that available information is predictive of future outcomes, actively open-minded thinkers are more likely than others to make accurate forecasts.

Because the input for Futures Studies is to a very high degree formulated as written words and
texts, methods which automate the processing of texts can substantially help Futures Studies.
At Shaping Tomorrow, we have developed a software system using Natural Language Processing
(NLP), a subfield of Artificial Intelligence, which automatically analyzes publicly available texts and
extracts future-relevant data from theses texts. This process can be used to study the futures.
This article discusses this software system, explains how it works with a detailed example, and
shows real-life applications and visualizations of the resulting data. The current state of this
method is just the first step; a number of technological improvements and their possible benefits
are explained. The implications of using this software system for the field of Futures Studies are
mostly positive, but there are also a number of caveats.

In the world, energy demand continues to grow incessantly. At the same time, there is a growing need to reduce CO 2 emissions, greenhouse effects and pollution in our cities. A viable solution consists in producing energy by exploiting renewable sources, such as solar energy. However, for the efficient use of this energy, accurate estimation methods are needed. Indeed, applications like Demand/Response require prediction tools to estimate the generation profiles of renewable energy sources. This paper presents an innovative methodology for short-term (e.g. 15 minutes) forecasting of Global Horizontal Solar Irradiance (GHI). The proposed methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of solar radiation samples collected for four years by a real weather station. Then GHI forecast, the output of the neural network, is given as input to our Photovoltaic simulator to predict energy production in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed.

In this paper, we present a Cluster-Based Approach (CBA) that utilizes the support vector machine (SVM) and an artificial neural network (ANN) to estimate and predict the daily horizontal global solar radiation. In the proposed CBA-ANN-SVM approach, we first conduct clustering analysis and divided the global solar radiation data into clusters, according to the calendar months. Our approach aims at maximizing the homogeneity of data within the clusters, and the heterogeneity between the clusters. The proposed CBA-ANN-SVM approach is validated and the precision is compared with ANN and SVM techniques. The mean absolute percentage error (MAPE) for the proposed approach was reported lower than those of ANN and SVM.

As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.

 Tool design is a specialized area of manufacturing engineering comprising the analysis, planning, design, construction, and application of tools, methods, and procedures necessary to increase manufacturing productivity.  To carry out these responsibilities, today's tool designer must have a working knowledge of machine shop practices, toolmaking procedures, machine tool design, manufacturing procedures and methods, as well as the more conventional engineering disciplines of planning, designing, engineering graphics and drawing, and cost analysis.

Smart Agriculture is a development that emphasizes the use of information technology in the farming. Most of the population in India depending on agriculture. This situation is one of the reason, that hindering the development of country. Nowadays, even though farmers get more yield for their crop but the market price for that crop will be less, in that case farmers get loss for their product and vice versa. Particularly, when growing new crops, farmers face the risks of both market price and production problems. To overcome these problems, a machine learning technology is used. Predictive analysis is a branch of data mining which predicts the future probabilities and trends. The prediction will help the farmers to choose whether the particular crop is suitable for specific rainfall and crop price values. This approach is to increase the net yield rate of the crop, based on rainfall. Prediction can be carried out by using various machine learning algorithms like linear regression, SVM, K NN method and decision tree algorithm out of which SVM is giving the highest efficiency. The predictive analysis technique can be implemented in several government sectors like APMC, kissan call center etc., by which the government and farmers can get the information of the future rainfall, crop yield and the market price.

The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship betwee...

The remarkable increase in per capita power consumption worldwide has drawn attention towards the required growth in renewable energy sector in order to bridge the gap between overall demand and supply. Solar power has been the fastest growing source of energy throughout the world outstripping all other forms of power generation. Though Solar PV system is recognized as promising renewable energy technology it is burdened with few uncertainties and drawbacks. To make interconnected grid system more resilient it is necessary to do solar energy generation forecast which is under acceptable limits. This paper presents a solar energy prediction model consisting of a mathematical model which enables to compute the amount of solar energy generation for next seven days (including present day) by considering weather data and plant specifications. The factors that affect the solar energy generation are ambient temperature, solar irradiance, efficiency of panels, and efficiency of plant components like: inverter, cables, type of PV module installation etc. The graphical results indicate the fine parallelism between predicted results and actual output concluding high accuracy except for the climate situations observed such as partly cloudy or overcast and during plant malfunctioning in terms of string failure.

THE ARTICLE DESCRIBES PROSPECTS FOR THE RUSSIAN-CHINESE RELATIONS IN THE CONTEXT OF THE FOREIGN POLICY OBJECTIVES SET BY THE 18TH CONGRESS OF THE CPC AND BY THE FOREIGN POLICY CONCEPT OF THE RUSSIAN FEDERATION, AS WELL AS IN LIGHT OF THE OUTCOME OF XI JINPING’S VISIT TO MOSCOW ON THE 22-23TH OF MARCH 2013. THE ARTICLE LIKEWISE ANALYZES THE POSSIBLE IMPACT OF LEADING FACTORS LIKE GEOGRAPHIC ADJACENCY, HISTORY OF RELATIONS, GEOPOLITICAL SITUATION, ON FURTHER DEVELOPMENT OF RELATIONS BETWEEN RUSSIA AND CHINA. THREE POSSIBLE SCENARIOS FOR THE EVOLUTION OF THESE BILATERAL RELATIONS IN NEXT TEN YEARS ARE PRESENTED.

Logistic regression analysis of factors contributing to bank deposit subscription and direct marketing success.

View project at:

https://github.com/JackKRasmus-Vorrath/Logistic_Regression_Direct-Marketing_Bank-Term-Deposit-Subscription

"Below are percentage ups and downs of the BTC(Bitcoin)/USD currency pair during times when the Sun and Mercury are in the same sign and also during times when the Sun and Mercury are in different signs. These statistics go back to 2012. My initial observation would conclude that there is a higher prevalence of a rise in BTC/USD when the Sun and Mercury are in different signs and a higher prevalence of a drop when the Sun and Mercury are in the same sign. Mercury retrograde periods, however, seem to reverse that pattern, making it to where the Sun and Mercury in different signs would drive the BTC/USD lower, while the Sun and Mercury in the same sign (during the retrograde) would bring it higher. Any of the stats marked below with an asterisk * are the dates that would be affected by the mercury retrograde"

This article examines five common misunderstandings about case-study research: (a) theoretical knowledge is more valuable than practical knowledge; (b) one cannot generalize from a single case, therefore, the single-case study cannot contribute to scientific development; (c) the case study is most useful for generating hypotheses, whereas other methods are more suitable for hypotheses testing and theory building; (d) the case study contains a bias toward verification; and (e) it is often difficult to summarize specific case studies. This article explains and corrects these misunderstandings one by one and concludes with the Kuhnian insight that a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one. Social science may be strengthened by the execution of a greater number of good case studies.

AUTOSAR XML (.arxml) is a format introduced by the Automotive Open System Architecture consortium to contain the data used in and required by Electric Control Units which is based on Automotive Open System Architecture. Electric Control Unit development is done by 3 groups i.e. OEM like BMW, Tier1like Bosch, and Tier2 like a vector. Each one has its own data that is required for Software development. OEM shares the information with Tier1.Tier1 shares the same with Tier2 for implementing into Electric Control Unit. The DataBase Container (DBC) describes the properties of the Controller Area Network (CAN) / Controllable Area Network Flexible Data Rate (CAN FD). Data analysis in ECU Extract ARXML is very difficult due to the complex structure of data. Performing two-way and three-way comparisons/merges of XML files is difficult. To handle the mentioned challenges manually is a big task that requires huge manual efforts and time which may even result in a manual error. In this paper, we are presenting a newly developed Python-based tool that overcomes the above-mentioned drawbacks.