Forecasting and Prediction Tools

The data mining its main process is to collect, extract and store the valuable information and now-a-days it's done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is composed of various analytical and statistical techniques used for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals with both continuous changes and discontinuous changes. It provides a predictive score for each individual (healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to determine, or influence the organizational processes which pertain across huge numbers of individuals, like in fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law enforcement.

Coastal wetlands provide diverse ecosystem services such as flood protection and recreational value. However, predicting changes in ecosystem service value from restoration or management is challenging because environmental systems are highly complex and uncertain. Furthermore, benefits are diverse and accrue over various timescales. We developed a generalizable mathematical coastal management model to compare restoration expenditures to ecosystem service benefits and apply it to McInnis Marsh, Marin County, California, USA. We find that benefits of restoration outweigh costs for a wide range of assumptions. For instance, costs of restoration range from 8-30% of the increase in ecosystem service value over 50 years depending on discount rate. Flood protection is the dominant monetized service for most payback periods and discount rates, but other services (e.g., recreation) dominate on shorter timescales (> 50% of total value for payback periods ≤4 years). We find that the range of total ecosystem service value is narrower than overall variability reported in the literature, supporting the use of mechanistic methods in decision-making around coastal resiliency. However, the magnitude and relative importance of ecosystem services are sensitive to payback period, discount rate and risk tolerance, demonstrating the importance of probabilistic decision analysis. This work provides a modular, transferrable tool to that can also inform coastal resiliency investments elsewhere.

Time series variables (e.g., presidential approval, public mood liberalism, GDP, inflation, education level) are extremely common in the social sciences. However, due to certain properties, these series cannot always be handled using standard regression approaches. This short course at the VU Amsterdam Graduate School serves as an introduction to the world of time series analysis and also aims to strenghten research studies with dynamic data analysis. Join us in January 2020!

To forecast wind speed is a crucial process to the design, the development and the operation of a windelectric plants. The speed and the direction of the wind are generally intermittent and it is necessary a robust model that be able to predict correctly their variations. This paper proposes a new wind speed forecasting model based on artificial neural networks and singular spectrum analysis. This model is able to provide forecasts from 1 to 24 hours ahead, and has been experimented using a Brazilian wind-power generation database. The obtained results have demonstrated that the model proposed in this paper is robust and reliable for real applications.

Purpose-Physical security has remained an important reason for, and the consequence of, societal living. In recent times, the threat potential and the risks of loss and damage due to criminal activities have increased substantially. In Indian Railways, it is being increasingly felt that efficient security management is inevitable for the development and sustainability of desired state of affairs. The purpose of this paper is to address the broad goal of achieving optimal strategies for minimizing security threats to Indian Railway Security System (IRSS). Design/methodology/approach-The authors use two forecasting techniques, namely, Delphi technique and Harva method, whose joint approach allows the authors to use both quantifiable (Delphi technique) and linguistic (Harva method) data. The choice of the two approaches provides a multi-method approach to the research problem. Findings-Predicted trend toward the expected scenario in 2020 has more or less matched with the actual developments for improvement in security scenario of Indian Railways. The positive indications are that there is an improving trend, which is expected to lead to a much better state of affairs with certain inputs. Research limitations/implications-The joint approach of Delphi technique and Harva method is a multi-method example of original research work in the railway security, which can also be implemented in other security settings, such as aviation or marine security. A replication of the exercise closer to the target date will throw light on the exact state of affairs in the area of railway security in India. Practical implications-An outcome in consonance with the present exercise has been the implementation of the policy developed on the basis of the forecasts. Policy efforts initiated in the recent past have been consistent with the features discussed in the study. From the above indicators, it can be inferred that some of the policy initiatives taken are in line with the trend status as predicted by the Delphi exercise. Social implications-Security has been considered as an important reason, as well as a consequence, of living in a society. It has been perceived as the condition of being protected against danger or loss, and also refers to the freedom from exposure to danger (protection), implying a feeling of assurance against danger. Research work in this area, thus, has strong social welfare implications. This is particularly so as the area of security gained importance, not only in India, but also across the world. Originality/value-The present study is the first of its kind in the area of railway security using systems approach. The approach used is quite generic and can also be implemented in other security settings, such as aviation or marine security.

With the introduction of autoscaling, clouds have strengthened their position as self-adaptive systems. Nevertheless , the reactive nature of the existing autoscaling solutions provided by major Infrastructure-as-a-Service (IaaS) cloud services providers (CSP) heavily limits the ability of cloud applications for self-adaptation. The major reason of such limitations is the necessity for the manual configuration of the autoscaling rules. With the evolution of monitoring systems, it became possible to employ the data-driven approaches to derive the parameters of scaling rules in order to enable the autoscaling in advance, i.e. the predictive autoscaling. The change in the amount of requests to microservices could be considered as a reason to adapt the virtual infrastructure underlying the cloud application. By forecasting the amount of requests to cloud application, it is possible to estimate the upcoming demand to replicate the microservices in advance. Hence, anticipation of the demand on the cloud application helps to evolve its self-adaptive properties. In the scope of the paper, the authors have tested various extrapolation models on the real anonymized requests time series data for 261 microservices provided by the industry partner Instana. The tested models are: various seasonal ARIMA models with GARCH modifications and outliers detection , exponential smoothing models, singular spectrum analysis (SSA), support vector regression (SVR), and simple linear regression. In order to evaluate the accuracy of these models, an interval score was used. The time required to fit and use each model was also evaluated. Comparative results of this research and the classification of forecasting models based on the interval accuracy score and model fitting time are provided in the paper. The study provides an approach to evaluate the quality of forecasting models to be used for self-adapting cloud applications and virtual infrastructure.

En base al primer modelo predictivo del voto en Argentina no basado en encuestas de intención de voto y aplicable antes de un comicio presidencial, se concluye que el gobierno de la coalición Cambiemos perderá las elecciones en 2019. El patrón existente desde 1989, según el cual ningún ganador de la elección previa pudo volver a ganar si al menos cuatro de los siete indicadores sobre la situación económica, social y política del país no le fueron favorables, permite arribar a dicha conclusión, dado que precisamente ese número de indicadores ya son negativos y por lo tanto le costarían al presidente Macri su reelección. El modelo había sido planteado a partir de Lichtman y Keilis Borok (1981): la existencia de una relación estadística fuerte y significativa entre las variables independientes y dependiente provee importante evidencia empírica a favor suyo.

The digital economy is highly volatile and uncertain. Ever-changing customer needs and technical progress increase the pressure on organizations to continuously improve and innovate their business processes. The ability to anticipate incremental and radical process changes required in the future is a critical success factor. However, organizations often fail to forecast future business process designs and process performance. One reason is that Business Process Management (BPM) is dominated by reactive methods (e.g., lean management, traditional process monitoring), whereas there are only a few future-oriented approaches (e.g., process simulation, predictive process monitoring). This paper supports the shift towards proactive BPM by coining the notion of process forecasting-an umbrella concept for future-oriented BPM methods and techniques. We motivate the need for process forecasting by eliciting various types of process forecasting from BPM use cases and create a first understanding of its scope by providing a definition, a reference process, showing the steps to be followed in process forecasting initiatives, and a positioning against related BPM sub-areas. The definition and reference process are based on a structured literature review.

Real Estate Promoter promoting the new apartments/villa around the city to sell and for rent. As an investment firm, I will be advising the people whether to invest on the property looking around the neighbourhood based on the amenities around the place. When it is coming to living, People always looks for some essential amenities that facilitates the living so as an investor firm it is their job to see the growth of the place over years and amenities near the location that helps the investment firm to take proper decision and help the customer in decision making.

Stock price forecast has long been received special attention of investors and financial institutions. As stock prices are changeable over time and increasingly uncertain in modern financial markets, their forecasting becomes more important than ever before. A hybrid approach consisting of two components, a neural network and a fuzzy logic system, is proposed in this paper for stock price prediction. The first component of the hybrid, i.e. a feedforward neural network (FFNN), is used to select inputs that are highly relevant to the dependent variables. An interval type-2 fuzzy logic system (IT2 FLS) is employed as the second component of the hybrid forecasting method. The IT2 FLS's parameters are initialized through deployment of the k-means clustering method and they are adjusted by the genetic algorithm. Experimental results demonstrate the efficiency of the FFNN input selection approach as it reduces the complexity and increase the accuracy of the forecasting models. In addition, IT2 FLS outperforms the widely used type-1 FLS and FFNN models in stock price forecasting. The combination of the FFNN and the IT2 FLS produces dominant forecasting accuracy compared to employing only the IT2 FLSs without the FFNN input selection.

Activity to predict sales multiple products intended for control of the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfilment of consumer demand can be cultivated in a timely and cooperation with suppliers maintained properly so that company can avoid losing sales and customers. This study aims to predict sales multiple products (6,877 products) using Single Exponential Smoothing (SES) approach, which is expected to improve the efficiency of the inventory system. Measurement accuracy of prediction in this study using a standard measurement Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results showed that the percentage of multi-product prediction accuracy using SES method is high, because the MAPE value obtained is 1.06% with the smoothing parameter α = 0.9.

Anomaly detection refers to the problem of finding patterns in data that do not confirm to expected behaviour. The survey tries to provide a structured and comprehensive overview of the research on anomaly detection. There are grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category there are identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behaviour. Further, for each category, identifying the advantages and disadvantages of the techniques in that category is a part of the projects goal. And find out the model which is best suited for detecting and prediction of anomaly from the telemetry data. The aim is to identify a simple automated process which only verifies if a certain parameter crosses upper or lower thresholds and is robust enough to detect the various types of anomalies that may occur in the telemetry data. We will be analysing data and use machine learning algorithms to attempt a realistic prediction, gain experience in machine learning algorithms and their applicability to satellite telemetry data analysis. Combining satellite data and machine learning algorithms to predict the dynamic characteristics of a satellite, such that an outcome is obtained with good accuracy rate.

Various studies have been carried out to evaluate forecasting methods most especially with a focus on moving average and exponential smoothing. The common approach for evaluating the accuracy of moving average and exponential smoothing were Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Root Mean Square Error (RMSE). This paper, therefore, examines single moving average and exponential smoothing and adopts the coefficient of description (correlation coefficient), coefficient of explanation (regression), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Root Mean Square Error (RMSE) to evaluate the accuracy of single moving averages and exponential smoothing with different smoothing constants. For single moving average: n=2, n=3, n=4, n=5, n=6, n=7, n=8, n=9, n=10; and for simple exponential smoothing: α=0.1, α=0.2, α=0.3, α=0.4, α=0.5, α=0.6, α=0.7, α=0.8, α=0.9. The study relies on secondary data of revenue passenger demand in Murtala Muhammed International Airport (MMIA) and Mallam Aminu Kano International Airport (MAKIA) from the period of 1995 to 2017. The behaviors of data obtained on different airports were observed, as there seems to be consistency in the MMIA demand than that of MAKIA demand. The implication of the consistency is that the result that emanates for forecast evaluation will be reliable. The study reveals that simple exponential smoothing generates a reliable forecast than the single moving average.

Purpose Understanding and predicting the behaviours of households within a community is a key concern for fire services as they plan to deliver effective and efficient public services. In this paper, an agent-based modelling approach is used to deepen understandings of changing patterns of behaviour within a community. Design/methodology/approach This "Premonition" model draws on historical data of fire incidents and community interventions (e.g. home safety checks, fire safety campaigns etc) collated by South Yorkshire Fire & Rescue, UK, to unpack patterns of changing household behaviours within the region. Findings Findings from simulations carried out using the Premonition model, show that by targeting close-knit groups of connected households, the effectiveness of preventative interventions and utilization of associated resources is enhanced. Furthermore, by repeating these interventions with the same households over time, risk factors within the wider area are further reduced. Originality/value The study thus shows that annual repeat visits to fewer and more targeted high-risk postcodes increases the overall reduction in risk within an area, when compared with a scattered coverage approach using one-off (i.e. not repeat) household visits within a postcode.

The life cycle of electronic products is becoming shorter. Calculate the optimum quantity to buy and hold in inventory is a big challenge, must be incorporated the temporary variable into the mathematical model that describes the death of the product and therefore calculate the required inventory. Economic Order Quantity (EOQ) was used in this case study in coordination with the logistic function, originally applied to population growth, this is a convenient function for estimating economic batch versus time analogy. Thus, a nonlinear regression was used to estimate the parameters of a hybrid model, EOQ-curve S. Once applied this model, the total costs of inventories were reduced by about 67%, demonstrating the effectiveness achieved.

Artificial Neural Networks have long been considered a simple yet powerful and elegant paradigm for solving problems related to Pattern Recognition, Machine Learning and Knowledge Discovery. However, performance of traditional, monolithic neural network based systems, suffers when faced with complex problems which involve a large number of decision variables or dimensions. Also, performance of any such system depends on the architecture of the neural network involved. The architecture usually remains sub-optimal as human expertise is generally used to design the optimal architecture. In this paper, we describe how the twin paradigms of modularity and swarm intelligence based optimization could be
successfully used to overcome these concerns. Here, instead of using a single monolithic expert, we use a modular neural network where several independent neural network experts individually work upon the inputs and give their outputs which is then integrated using an Integrator (here, a Fuzzy C-Means
Integrator). Also, swarm intelligence has been used to determine the connections in each individual expert
for achieving an optimized architecture for each expert. This approach has been used for the diagnosis of breast cancer disease. Experimental results show that the proposed approach gives a better diagnostic ability than those of other traditional methods used.

The purpose of this quantitative study was to investigate the observable patterns of online search behavior in the topic of leadership using Google Trends data. Institutions have had a historically difficult time predicting good leadership candidates. Better predictions can be made by using the big data offered by groups such as Google to learn who, where, and when people are interested in leadership. The study utilized descriptive, comparative, and correlative methodologies to study Google users’ interest in leadership from 2004 to 2017. Society has placed great value into leadership throughout history, and though overall interest remains strong, it appears that the expression of that interest may have changed over time. Key findings revealed that interest in leadership often peaks during the spring and fall seasons while dipping during the summer and the winter holiday seasons. Leadership interest also appears to be more concentrated in geographic locations that home certain universities and political arenas.

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (C c) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict C c through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e 0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate C c. This study presents a novel prediction model for the C c of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C c based on LL, PL, and e 0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R 2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R 2 , RMSE, and MAE compared to the other models.

Rapid growth is seen in health care services over the past few years. Heart disease causes millions of death worldwide. Many wireless communication technologies have been developed for heart disease prediction. Data mining algorithms are very useful in the detection and diagnosis of heart disease. In this paper, survey is carried out on several single and hybrid data mining algorithms in order to identify the algorithm that best suits the heart disease prediction with high level of accuracy.