There are many multi-criteria methods for ranking and classifying customers, but it is difficult to determine which, if any, ranking method is the best. In this paper, we propose an application that uses several ranking methods, is easy to implement and retrieves ranking criteria values from the customer relationship management system. Because each ranking method can yield a different ranking for each customer, we suggest giving each ranking method the same weight so that each customer's final ranking will be determined by the average of the ranks obtained from all the ranking methods. A unique result of the proposed application is the possibility of calculating the variance of the rank for each customer and the confidence intervals. The applicability of the proposed method was demonstrated in a real case study with nine ranking methods; IPython codes of five of those methods are available herein.

Electric vehicles (EVs) have become popular in the last decade because of their advantages compared to conventional vehicles. The market offers dozens of EV models in a large range of prices, performances, and specifications. This paper presents an expert system we developed to support sellers and customers in choosing an EV that matches the customers’ specifications. The system enables ranking-specific EVs according to the customers’ specifications and counting the number of mismatches. The paper analyzes a database of 53 different EVs, each with 22 different characteristics, enabling customers to choose the EV that best suits their most important specifications. Based on the customer’s requirements and the principle of fuzzy sets, the system assigns a matching value to each criterion. These matching values are the input matrix for the TOPSIS procedure that ranks all the EVs according to their matching scores for a specific customer. The applicability of the proposed method is demonstrated for one customer with specific preferred EV requirements. A Python code of this method is also available herein.

The operation and maintenance of modern aircraft multi-sensor data fusion systems generate vast amounts of numerical and symbolic data. Learning useful and non-trivial insights from this data may lead to considerable savings, and detection and reduction of the number of faults, as a result increasing the overall level of aircraft safety. Several machine learning techniques exist to learn from big amounts of data. However, the use of these techniques to infer the desired readable and accurate interval regression tree models from the data obtained during the operation and maintenance of aircraft is extremely challenging. Difficulties that need to be addressed include data warehouse collection and preprocessing, machine learning model readability, setup, evaluation, and maintenance. This article presents the interval gradient prediction tree algorithm (INGPRET), which addresses these issues. As shown by the empirical evaluation of a real aircraft multi-sensor data set, the INGPRET algorithm provides better readability and similar performance in comparison to other machine learning algorithms.

The operation and maintenance of modern aircraft multi-sensor data fusion systems generate vast amounts of numerical and symbolic data. Learning useful and non-trivial insights from this data may lead to considerable savings, and detection and reduction of the number of faults, as result increasing the overall level of aircraft safety. Several machine learning techniquesexist to learn from big amounts of data. However, the use of thesetechniques to infer the desired readable and accurate interval regression tree models from the data obtained during theoperation and maintenance of aircraft is extremely challenging. Difficulties that need to be addressed include: data warehouse collection and preprocessing, data labeling, machine learning model readability, setup, evaluation and maintenance. This paper presents the Interval Gradient Prediction Tree algorithm INGPRET, which addresses these issues. As shown by our empirical evaluation of a real aircraft multi-sensor data set, the INGPRET algorithm provides better readability and similar performance in comparison to other regression tree machine learning algorithms.

A significant part of pension savings is in the capital market and exposed to market volatility. The COVID-19 pandemic crisis, like the previous crises, damaged the gains achieved in those funds. This paper presents a development of open-source finance system for stocks backtesting trade strategies. The development will be operated by the Python programming language and will implement application user interface. The system will import historical data of stocks from financial web and will produce charts for analysis of the trends in stocks price. Based on technical analysis, it will run trading strategies which will be defined by the user. The system will output the trade orders that should have been executed in retrospect and concluding charts to present the profit and loss that would occur to evaluate the performance of the strategy.

The Business Intelligence Project-Oriented Course has been taught in the department of Industrial Engineering and Management since 2016. In this course, the students learn to build websites and business intelligence systems which enable to perform data analysis and research in order to get valuable business insights and to retrieve specific business information. The article is devoted to BI course implementation in the Department of Industrial Engineering and Management of Sami Shamoon College of Engineering (SCE).

In this chapter, the authors introduce the interval sliding window (ISW) and interval sliding window and bottom up (ISWAB) algorithms, which are applicable to big data numerical time series data streams and use as input the confidence level parameter rather than the maximum error threshold. The proposed algorithms have two advantages: first, they allow performance comparisons across different time series data streams without changing the algorithm settings, and second, they do not require preprocessing the original time series data stream in order to determine heuristically the reasonable error value. These improvements are very efficient and important in context of big data tine series data streams processing. Finally, an empirical evaluation was performed on two types of time series data.

Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non - seasonal and two seasonal sliding window-based ARIMA (Auto Regressive Integrated Moving Average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load. The algorithms integrate non - seasonal and seasonal ARIMA models with the OLIN (Online Information Network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.

This paper presents Interval Prediction Tree INPRET algorithm for interval prediction of numerical target variables from temporal mean-variance aggregated data. The proposed algorithm allows to process mean-variance aggregated multivariate temporal data and to identify outliers in training data instances. The proposed algorithm enables, on the one hand, to utilize predictive feature information obtained from mean and variance of temporally aggregated instances, and on the other hand, to achieve a considerable reduction in the depth of the induced prediction tree by using interval prediction tree leaves. As shown by our empirical evaluation of aircraft maintenance real world multi-sensor data set, in terms of the prediction tree size and the root mean square error, the proposed algorithm provides better integration between accuracy and performance than existing regression tree models.

Interval prediction can be more useful than single value prediction in many continuous data streams.This paper introduces a novel Interval Prediction Tree IP3 algorithm for interval prediction of numerical target variables from temporal mean-variance aggregated continuous data.This algorithm characterized by: processing incoming meanvariance aggregated multivariate temporal data, splitting each of the continuous features of the input according to the best mean-variance and making stable interval predictions of a target numerical variable with a given degree of statistical confidence.As shown by empirical evaluations in forest fires data set the proposed method provides better performance than existing regression tree models.

The real-world data process of large spatio-temporal data collection presents a very difficult technical problem. Firstly, the given process is very expensive, requiring a lot of various high-technology software instruments and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; secondly, this process sometimes cannot show special traffic patterns, which we may characterize as patterned traffic trajectories. The Arena simulation framework introduced in this paper uses our suggested random linear interpolation algorithm and spatio-temporal prediction algorithm, which are applicable to visualize, handle and predict movement data with various time resolutions.

Big data time series data streams are ubiquitous in finance, meteorology and engineering. It may be impossible to process an entire “big data” continuous data stream or to scan through it multiple times due to its tremendous volume. In Heraclitus’s well-known saying, “you never step in the same stream twice,” and so it is with “big data” temporal data streams. Unlike traditional data sets, big data continuous data streams flow into a computer system continuously, in a non-stationary way and with varying update rates. They are time-stamped, fast-changing, massive, and potentially infinite. Under these circumstances, they represent an application area of growing importance in the data mining research. For example, sensors generate one million samples every minute (Hulten & Domingos, 2003) therefore the primary purpose of time series data stream segmentation is dimensionality reduction. This technique is used in many areas of data stream mining as: frequent patterns finding, structural changes and concept drifts detection (Ge & Smyth, 1999), time series classification and prediction (Hulten & Domingos, 2003), time series similarities searching (Keogh, Chakrabarti, Pazzani, & Mehrotra, 2000), (Park, Kim, & Chu, 2000), etc. The main principle of segmentation algorithms concludes in reducing the big data time series dimensionality by dividing the time axis into intervals behaving approximately according to a simple model. A good big data time series data stream segmentation algorithm must be OFASC (Online, Fast, Accurate, Simple and Comparable). For example the Sliding Window algorithm (Keogh, Chu, Hart, & Pazzani, 2004) on the one hand is online (O), very fast (F) and relatively simple (S) for using in online segmentation applications but on the other hand, it sometimes gives poor accuracy (A) and does not allow to perform online multivariate segmentation (C). Therefore, we will classify this algorithm to OFS segmentation algorithms domain. The segmentation problem can be defined in following way: first, given a time series data stream to produce the best representation such that the maximum error for any segment does not exceed some user specified confidence level error threshold. It is important to add, that using a relative parameter such as confidence level will allow to evaluate an online multivariate segmentation and second, to construct a user friendly segmentation application which will evaluate and compare the proposed online segmentation algorithms in real time. As we shall see in later sections, the stateof-the-art segmentation algorithms do not meet all these requirements. The rest of the paper is organized as follows. In Section 2, we provide a literature review of three state-of-the-art online piecewise linear segmentation algorithms. In Section 3, we provide a methodology for improving the existing stateof-the-art online segmentation algorithms. The proposed methodology based on Hoeffding bound error estimation, which uses a relative probability parameter instead of maximum error nominal parameter and meets the proposed OFASC reDima Alberg SCE Shamoon College of Engineering, Israel

This paper presents an advanced review of regression tree methods for mining data streams. Batch regression tree methods are known for their simplicity, interpretability, accuracy, and efficiency. They use fast divide‐and‐conquer greedy algorithms that recursively partition the given training data into smaller subsets. The result is a tree‐shaped model with splitting rules in the internal nodes and predictions in the leaves. Most batch regression tree methods take a complete dataset and build a model using that data. Generally, this tree model cannot be modified if new data is acquired later. Their successors, the incremental model and interval trees algorithms, are able to build and retrain a model on a step‐by‐step basis by incorporating new numerical training instances into the model as they become available. Moreover, these algorithms produce even more compact and accurate models than batch regression tree algorithms because they use intervals or functional models with a change detection mechanism, which makes them a more suitable choice for regression analysis of data streams. Finally, this review summarizes the performance results of the reviewed methods and crystallizes 10 requirements for successful implementation of a regression tree algorithm in data stream mining area. © 2011 Wiley Periodicals, Inc.

In this paper we introduce the ISW (Interval Sliding Window) algorithm, which is applicable to numerical time series data streams and uses as input the combined Hoeffding bound confidence level parameter rather than the maximum error threshold. The proposed algorithm has two advantages: first, it allows performance comparisons across different time series data streams without changing the algorithm settings, and second, it does not require preprocessing the original time series data stream in order to determine heuristically the reasonable error value. The proposed algorithm was implemented in two modes: off line and online. Finally, an empirical evaluation was performed on two types of time series data: stationary (normally distributed data) and non stationary (financial data).

In this paper, we present a novel method for fast data-driven construction of regression trees from temporal datasets including continuous data streams. The proposed Mean Output Prediction Tree (MOPT) algorithm transforms continuous temporal data into two statistical moments according to a user-specified time resolution and builds a regression tree for estimating the prediction interval of the output (dependent) variable. Results on two benchmark data sets show that the MOPT algorithm produces more accurate and easily interpretable prediction models than other state-of-the-art regression tree methods.

The real-world process of generating a large spatio-temporal data collection presents a very difficult technical problem. First, this process is very expensive, requiring a lot of various high-technology software tools and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; second, the recorded trajectories sometimes cannot represent any special traffic or movement patterns. The simulation framework introduced in this paper can generate diverse trajectory datasets based on predetermined movement patterns.

A comprehensive empirical analysis of the mean return and conditional variance of Tel Aviv Stock Exchange (TASE) indices is performed using various GARCH models. The prediction performance of these conditional changing variance models is compared to newer asymmetric GJR and APARCH models. We also quantify the day-of-the-week effect and the leverage effect and test for asymmetric volatility. Our results show that the asymmetric GARCH model with fat-tailed densities improves overall estimation for measuring conditional variance. The EGARCH model using a skewed Student-t distribution is the most successful for forecasting TASE indices.

This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions

Anomaly detection is an important tool for detecting abnormal, potentially criminal behavior in real-world data such as web documents, credit card transactions or other personal data. In this study, we assume having two labeled collections of normal and anomalous documents downloaded from normal and terrorist web sites respectively. Each document is represented by a set of keyphrases (words or short expressions) and their associated weights. By using a clustering method two separate sets of centroids are induced. In this paper, we propose a lexicon-based approach for labeling a new incoming document. It compares the document's list of keyphrases with two strict lexicons - the disjoint sets of keyphrases used only in the normal or the terrorist documents respectively and a common lexicon of the remaining keyphrases. If a classification cannot be derived from the strict lexicons, the document is labeled based on its distances from both sets of centroids.