Sharif Digital Repository

According to the rise of turnover and pace of trading, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases so, using machines to analyze big data seems critical. Hence, financial markets tend to apply algorithmic trading in which some techniques like data mining and machine learning are notable. OLPS which sequentially allocates capital among a set of assets aiming to maximize the final return of investment in the long run, is the core problem in algorithmic trading. This article presents an online portfolio selection algorithm. The online portfolio selection sequentially selects a portfolio over a set of assets... 

Obtaining high profit is the ultimate goal of an investor in the financial market. The key to achieve high profits in stock trading is to find the right time to trade with minimum business risk. However, it is difficult, often, to make decision about the best time to buy or sell some stocks due to the extremely dynamic and volatile behavior of the stock market. In order to resolve these problems, two steps have been followed in this research:1) Create a model to predict the final price of the stock with small error rate, and 2) Suggest the best stocks for trading to the trader. In order to achieve the goals of the first step, the stock price data of Hcltech, Maruti, Axisbank is selected and... 

Today, many real-world datasets such as social network data and web pages can be shown as graphs. Community detection and clustering of these big graphs has many applications in different fields like recommender systems in social networks and Diag- nosis of diseases in communication networks among proteins. A cluster in a graph is a sub-graph with many internal and few external edges. A new method for local cluster detection around an existing vertex is introduced in this paper. This method applies random walk algorithm for cluster detection. The time complexity of this algorithm based on the graph size is polynomial. Therefore, it can be used for clustering of big graphs. The experimental... 

Nowadays, capital markets play a key role in the economies of countries. Hence, this market is expanding more and more every day. In such circumstances, traditional analysis methods such as fundamental analysis and technical analysis have lost their position due to low speed and accuracy. In recent years, automated trading systems have been proposed as a solution to these problems. The online portfolio selection, which sequentially allocates capital among a set of assets aiming to maximize the final return of investment in the long run, is the core problem in algorithmic trading. In this research, we present an online portfolio selection algorithm based on pattern matching principle.... 

In this thesis, a hybrid method is proposed to estimate the change point in the parameters of simple linear profiles in multistage processes under monotonic changes. In monotonic changes, the type of change is not known a priori, and the only assumption is the changes are of non-decreasing (isotonic) or non-increasing (monotonic) type. In the proposed method, at first, the stages and the parameters experiencing the change are identified and then, the changes occurred in these stages and parameters are identified and examined based on the moving window approach and support vector machine (SVM) algorithm. Finally, the maximum likelihood estimator of the change point is proposed. The... 

Since the start of Covid-19 pandemic lately in 2019 from Wuhan in China, a lot of countries encountered it. Healthcare sysytems are the most important system against pandemics so it is needed to measure the efficiency of healthcare systems against Covid-19 in order to find best practices. In this research, a 3-phased method is proposed to evaluate the performance of the healthcare systems. In the first phase, countries are clustered, in the second phase the DEA model is applied in 2 separate parts, in one part with considering clusters and in another without it. In the third phase resilience is introduced for Covid-19 and then it is used as a criterion beside two other criteria, DEA result... 

Nowadays, investigating the factors affecting traffic models from different aspects such as metropolitan planning according to the present conditions can help high-level decision-makers and also, at the micro-level, help the travelers to make appropriate decisions for scheduling affairs, route selection, and vehicle type selection. Given the importance of this topic, a framework will be presented in this study that will evaluate the impact of some identified factors such as travel distance, climate, and urban events, and then all these factors will be presented in mathematical formulas. In the end, based on the model, the travel time will be predicted. In this framework, gene expression... 

Algorithmic trading in financial markets with machine learning is a developing and promising field of research. Financial markets have a complex, uncertain, and dynamic nature, making them challenging for algorithmic trading. To cope with the challenges of algorithmic trading in financial markets, we propose a multi-agent deep reinforcement learning framework trained by Deep Q-learning (DQN) algorithm to perform financial trading. This framework consists of multiple cooperative agents, each of which trained on a specific timeframe, to perform financial trading on the collective intelligence of the agents. Numerical experiments are conducted on historical data of the EUR/USD currency pair.... 

Performing analysis and forecasting is crucial. Better forecasting will lead to better decisions. One method for predicting the future is time series analysis. In reality, it is common for an intervention to occur and alter the characteristics of a time series. In recent years, interrupted time series analysis has been receiving a lot of attention. A new forecasting method for interrupted time series has been developed in this study. This is a system dynamics-based approach. At every stage of the approach, system thinking is incorporated. In order to model the effects of a given intervention, common modes of behavior in dynamic systems are used. Furthermore, control theory has been used to... 

Graphs are a language to describe and analyze connections and relations. Recent developments have increased graphs' applications in real-world problems such as social networks, researchers' collaborations, and chemical compounds. Now that we can extract graphs from real life, how can we model and generate graphs similar to a set of known graphs or that are very likely to exist but haven't been discovered yet? Therefore, this research will focus on the problem of graph generation. In graph generation, a set of graphs is a training dataset, and the goal of the thesis is to present an improved deep generative model to learn the training data's distribution, structure, and features.Identifying... 

It has always been a challenge for retailers to plan which of the available goods will be displayed to the customer and at what price for each. In practice, the limited storage capacity of goods, the limited capacity of shelves, or the limited capacity of displaying goods on a web page in online stores may make it more difficult to decide on the above issues. These issues have been addressed in the literature when demand for goods is clear or a good estimate of demand can be obtained based on sales data. The purpose of this study is to investigate multi period Assortment planning, pricing and inventory planning with respect to the limited capacity of storage and display of goods in a... 

Since investing in the stock market is always known as one of the ways to increase capital, many researches have been done in the field of portfolio selection in order to provide methods to earn more profit and control investment risk. One of the influential factors in increasing the profit from the portfolio is the proper prediction of future of shares. Therefore, in many research, various methods and tools have been used to predict the future of shares more accurately. One of these tools is forecasting the market regime. In this research, harmonic patterns have been used to predict the market regime. Also, based on the harmonic patterns, the scope of entry into the transaction, the... 

One of the challenging issues in machine learning is the problem of data imbalance. Data imbalance occurs when the number of samples from one or more classes significantly exceeds or falls behind that of other classes. The existence of data imbalance in a dataset often leads to misleading accuracy of models, inadequate prediction of minority class, and a lack of generalization. Data imbalance can be observed in various datasets such as fraud detection, disease diagnosis, email spam detection, and fake news detection. To address this issue, various methods have been proposed, categorized into four groups: data-level, algorithm-level, cost-sensitive, and ensemble approaches. In this study, two... 

Single image super-resolution (SISR) is a known difficult problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.The problem of Super Resolution has applications in many areas and industries where, for example, it can reduce the cost and time of re-imaging in many fields including the medical industry. In the satellite industry, distortions are eliminated and geographic information increases with clarity. In the astronomical industry, image recognition and computation become easier with super resolution. Also in many other important fields, for example, car license plate imaging, image quality enhancement can have significant effects for... 

Now a day, stock price prediction is known as one of the most challenging activities in the financial field. Research in price prediction models in financial markets, despite its many challenges, is still one of the most active areas for research. The price of non-linear financial assets is dynamic and unpredictable. Therefore, it is very difficult to arrangement and predict financial time series. Recently, many studies demonstrate that checking the news published in relation to a stock can significantly improve the accuracy of the prediction model.Among the latest techniques available for stock price prediction, we can mention deep learning models, which due to their high ability to... 

Today, e-commerce systems are used by both types of users. Therefore, the systems will be exposed to systematic fraud, and fraud is one of the main sources of financial losses for organizations. Therefore, it is very important for organizations to use accurate methods to detect fraud. this field is one of the most important applications of data mining in finance. There are various challenges in fraud detection projects, and this research has divided these challenges into three categories, which are: data pre-processing due to the imbalance data set, the accuracy of the machine learning model, and uncertainty. In the first part, both oversampling and undersampling methods will be used in... 

Using an efficient method to manage inventory systems is always a challenging issue in supply chain optimization. In supply chains including perishable goods, it is possible to reduce waste and other costs by identifying uncertain demand patterns and managing inventory levels at different stages of the supply chain. Considering the uncertainty and complex conditions of supply chains in the real world, in order to create a suitable model to express these conditions, various uncertain factors must be considered, each of which affects the supply chain inventory level in some way. In this research, a multi-level perishable supply chain model with uncertain demand, lead time and deterioration... 

By considering the environmental factors, outpatient scheduling has become more efficient, and the closer these factors are to the actual situation, the more accurate the scheduling is. This study aims to schedule outpatients in such a way that in addition to bringing the hypotheses of the problem closer to the real world, its implementation can be done more easily. On the other hand, medical centers, due to their nature, do not consider the income in their objective function. Therefore, another contribution of this study is considering the income of medical centers in such a way that not only will it increase patient satisfaction, but also it maximizes the revenue of the medical center.... 

Nowadays, researchers working on classifiers which are designed to predict minority class. In this work, we attempt to improve fraud detection performance, with minimum possible complexity. In this regard, by incrementing model sensitivity to minority class samples, we solve the problem of model ignorance to these instances. Moreover, by using clustering, we cluster similar inputs based on their features, and split each class to smaller bins. Then with considering the fact that, prediction probability threshold influences the final performance, we define statistical hypothesis testing exclusively for each cluster to evaluate predictions with expected range. In this method, model is not... 

Recommending routes to city taxis in order to improve their performance in finding passengers can reduce traffic, pollution and waiting time for passengers, as well as this, increasing the efficiency of taxis can increase their income. In this study, by analyzing trajectory data obtained from 536 taxis in San Francisco over a period of one month, we generated networks of cells, each of which has time-dependent characteristics such as the probability of finding a passenger, the capacity of each cell, or the amount of demand exists is in the cell and the average speed of passing through that cell. also, the connections between these cells in order to make more use of the experiences of taxi...