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An AI Based Cryptocurrency Trading System
, M.Sc. Thesis Sharif University of Technology ; Khayyat, Amir Ali Akbar (Supervisor)
Abstract
Cryptocurrencies are not only regarded as a trustworthy method of financial transaction validated by a decentralized cryptographic system as opposed to a centralized authority, but also as one of the most popular and lucrative forms of trade and investing. Predicting the price of a cryptocurrency is a challenging topic in time-series research. Its intricacy is due to the volatility and large swings of cryptocurrencies' price. The emergence of brand-new cryptocurrencies, which might present a profitable trading opportunity but lack sufficient historical data for technical analysis, prompted us to develop a trading strategy that could be applied universally. The forecast of the next timestep's...
The Application of Deep Learning Models in Estimating the Energy of Residential Buildings
, M.Sc. Thesis Sharif University of Technology ; Rafiee, Majid (Supervisor) ; Shavandi, Hassan (Co-Supervisor)
Abstract
Electricity consumption has increased dramatically in recent decades, and this increase has severely affected electricity distribution. Therefore, forecasting electricity demand can provide a precondition for distributors. Predicting power consumption requires many parameters to be considered.In this research, machine learning, and deep learning methods such as recursive neural networks, long short-term memory networks, etc., as well as the ARIMA model will be used. These models have been tested on the London Smart Measurement Database. In order to evaluate the capability of the models in forecasting electricity consumption, each has been used to predict the electricity consumption of a...
Portfolio Formation Using Deep Learning
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Throughout history, forming an optimal asset portfolio has been the primary goal of capital owners and managers of investment funds in any economic activity. Achieving this goal is equivalent to trying to minimize the risk caused by the inevitable fluctuations in the capital market and maximizing the overall investment return during the expected period. Investors can operate in various financial markets where there are different stocks and asset classes in each of these markets. The main goal of investors is to identify profitable stocks and form an optimal asset portfolio based on them.Based on this, during the past decades, many studies have been conducted to form and optimize the stock...
A Machine Learning and Time-Frequency Domain Combined Approach for Improving Stock Portfolio Management
, Ph.D. Dissertation Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Price prediction in financial markets is an exciting problem for a vast majority of groups and people; however, investment portfolio managers and owners are always looking for holistic predic-tion approaches and tools having high functional accurate metrics. Strictly speaking, players in fi-nancial markets are always in search of methods and toolboxes since they need to overcome the un-certainty of their buy, sell, or hold decisions in order to reduce the investment risk. In this research, we have tried to deal with the stock price prediction problem as an asset pricing problem and find a novel approach to push forward the state-of-the-art of the problem based on the fundamental pric-ing...
Feature Extraction for Financial Markets’ Transactions
, M.Sc. Thesis Sharif University of Technology ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
Abstract
The use of machine learning and deep learning tools to predict the future behavior of trends in massive data requires the extraction and creation of the eigenvector for the chosen model in the problem. It should be noted that simply by increasing the number of features, it cannot be expected that the learning model will have a higher efficiency. Rather, the quality and importance of the features in the field under study should be carefully considered. Topics such as data redundancy, data correlation, the amount of information in the data, distorted data, outliers, etc. are important steps in improving the dataset and creating a feature vector for training the learning model. In the realm of...
Development of Macro-Level Crash Prediction Models, using Advanced Statistical and Machine Learning Methods
, Ph.D. Dissertation Sharif University of Technology ; Nassiri, Habibollah (Supervisor)
Abstract
Road casualty is the fifth leading cause of death in Iran. To adopt proper countermeasures there is a need to evaluate the consequences of the implemented policies. Despite the development of crash time series models, these methods have not been in accordance with the multivariate, seasonal, and non-linear nature of crash data. On the other hand, the interpretable crash causal analysis frameworks are descriptive and they lack predictive power. Moreover, the unobserved homogeneity between observations has been widely overlooked in the crash causal analysis literature. This thesis introduces a novel causal analysis methodology by combining the interpretability and prediction power of the...
Stock Price Prediction Based on Shareholders Trading Behavior
, M.Sc. Thesis Sharif University of Technology ; Habibi, Jafar (Supervisor)
Abstract
Nowadays, the capital market has a significant impact on the economy of a country and causes economic dynamism and growth in gross production. Among the important phenomena in the stock market is stock pricing, the correctness or incorrectness of which has a significant role in the performance of the stock market and the value of companies. The stock price in the stock exchange represents the stock market value and usually represents the investment value of the shareholders. Forecasting the trend of the stock market is considered an important and necessary thing and has been given much attention, because the successful forecasting of the stock price may lead to attractive profits by making...
Housing Market’s Cycles and Its Realtion to Economic Business Cycles in Iran
, M.Sc. Thesis Sharif University of Technology ; Fatemi, Farshad (Supervisor) ; Barakchian, Mahdi (Co-Advisor)
Abstract
Implying non-model based approach and using seasonal data, we determined cyclical component of housing market and discussed about its lagging or leading behavior to overall economic business cycles in Iran. To extract cyclical component we applied band pass filters, including Hodrick-Prescot, Baxter- King, Butterworth and Christiano-Fitzjerald, and Bry-Boschan’s algorithm on any time series which is able to explain housing market’s behavior in aggregate level. After examining many criteria we found that real residential investment in urban cycles depict cyclical behavior of housing investment. Real residential investment’ cycles in urban lag monetary base rate cycles and m1 rate cycles which...
Identifying the Main Factors Affecting Road Accidents in Iran Through Data Mining, Determining the Optimal Solution in Mitigation and Forecasting its Effectiveness Through Arima Models
, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
Road accidents are unfortunate events that cause more thanl16000 deaths each year in Iran. Intercity accidents require a comprehensive plan to reduce casualties because the number of roads users are increasing and the accidents account for nearlyl65% of fatalities. In this study, we first tried to identify the status of Iran through a study of traffic accidents in the world, and then the research and activities carried out in Iran were analyzed to find new and effective solutions. Using the daily fatalities data froml2008 tol2014, and using the new methodology presented in this research based on the Discrete Fourier Transformation (DFT), the Box-Jenkins models and the Secant method, the...
Chaos Control in Continuous Time Systems Using Delayed Phase Space Constructed by Takens’ Embedding Theory
, M.Sc. Thesis Sharif University of Technology ; Salarieh, Hassan (Supervisor)
Abstract
This research has dedicated to study the control of chaos when the system dynamics is unknown and there are some limitations on measuring states. There are many chaotic systems with these features occurring in many biological, economical and mechanical systems. The usual chaos control methods do not have the ability to present a systematic control method for these kinds of systems. To fulfill these strict conditions we have employed Takens embedding theory which guarantees the preservation of topological characteristics of the chaotic attractor under an embedding named "Takens transformation". Takens transformation just needs time series of one of the measurable states. This transformation...
Reconstruction procedure for writing down the langevin and jump-diffusion dynamics from empirical uni- and bivariate time series
, Article Understanding Complex Systems ; 2019 , Pages 215-226 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this chapter we present the steps of reconstruction procedure for writing down the Langevin and jump diffusion stochastic dynamical equations for uni- and bivariate time series, sampled with time intervals τ. © 2019, Springer Nature Switzerland AG
Reconstruction of stochastic dynamical equations: exemplary diffusion, jump-diffusion processes and lévy noise-driven langevin dynamics
, Article Understanding Complex Systems ; 2019 , Pages 227-241 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this chapter we reconstruct stochastic dynamical equations with known drift and diffusion coefficients, as well as known properties of jumps, jump amplitude and jump rate from synthetic time series, sampled with time interval τ. The examples have Langevin (white noise- and Lévy noise-driven) and jump-diffusion dynamical equations. We also study the estimation of the Kramers–Moyal coefficients for “phase” dynamics that enable us to investigate the phenomenon of synchronisation in systems with interaction. © 2019, Springer Nature Switzerland AG
Stochastic processes with jumps and non-vanishing higher-order kramers–moyal coefficients
, Article Understanding Complex Systems ; 2019 , Pages 99-110 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this chapter we study stochastic processes in the presence of jump discontinuity, and discuss the meaning of non-vanishing higher-order Kramers–Moyal coefficients. We describe in details the stochastic properties of Poisson jump processes. We derive the statistical moments of the Poisson process and the Kramers–Moyal coefficients for pure Poisson jump events. Growing evidence shows that continuous stochastic modeling (white noise-driven Langevin equation) of time series of complex systems should account for the presence of discontinuous jump components [1–6]. Such time series have some distinct important characteristics, such as heavy tails and occasionally sudden large jumps....
The Kramers–Moyal coefficients of non-stationary time series and in the presence of microstructure (measurement) noise
, Article Understanding Complex Systems ; 2019 , Pages 181-189 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
Most real world time series have transient behaviours and are non-stationary. They exhibit different type of non-stationarities, such as trends, cycles, random-walking, and generally exhibit strong intermittency. Therefore local stochastic characteristics of time series, such as the drift and diffusion coefficients, as well as the jump rate and jump amplitude, will provide very important information for understanding and quantifying “real time” variability of time series. For diffusive processes the systems have a longer memory and a higher correlation time scale and, therefore, one expects the stochastic features of dynamics to change slowly. In contrast, a rapid change of dynamics with...
Distinguishing diffusive and jumpy behaviors in real-world time series
, Article Understanding Complex Systems ; 2019 , Pages 207-213 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
Jumps are discontinuous variations in time series and with large amplitude can be considered as an extreme event. We expect the higher the jump activity to cause higher uncertainty in the stochastic behaviour of measured time series. Therefore, building statistical evidence to detect real jump seems of primary importance. In addition jump events can participate in the observed non-Gaussian feature of the increments’ (ramp up and ramp down) statistics of many time series [1]. This is the reason that most of the jump detection techniques are based on threshold values for differential of time series. There is not, however, a robust method for detection and characterisation of such discontinuous...
Reconstruction Procedure for Writing Down the Langevin and Jump-Diffusion Dynamics from Empirical Uni- and Bivariate Time Series
, Article Understanding Complex Systems ; 2019 , Pages 215-226 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this chapter we present the steps of reconstruction procedure for writing down the Langevin and jump diffusion stochastic dynamical equations for uni- and bivariate time series, sampled with time intervals τ. © 2019, Springer Nature Switzerland AG
Reconstruction of stochastic dynamical equations: exemplary diffusion, jump-diffusion processes and lévy noise-driven langevin dynamics
, Article Understanding Complex Systems ; 2019 , Pages 227-241 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this chapter we reconstruct stochastic dynamical equations with known drift and diffusion coefficients, as well as known properties of jumps, jump amplitude and jump rate from synthetic time series, sampled with time interval τ. The examples have Langevin (white noise- and Lévy noise-driven) and jump-diffusion dynamical equations. We also study the estimation of the Kramers–Moyal coefficients for “phase” dynamics that enable us to investigate the phenomenon of synchronisation in systems with interaction. © 2019, Springer Nature Switzerland AG
Stochastic processes with jumps and non-vanishing higher-order kramers–moyal coefficients
, Article Understanding Complex Systems ; 2019 , Pages 99-110 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this chapter we study stochastic processes in the presence of jump discontinuity, and discuss the meaning of non-vanishing higher-order Kramers–Moyal coefficients. We describe in details the stochastic properties of Poisson jump processes. We derive the statistical moments of the Poisson process and the Kramers–Moyal coefficients for pure Poisson jump events. Growing evidence shows that continuous stochastic modeling (white noise-driven Langevin equation) of time series of complex systems should account for the presence of discontinuous jump components [1–6]. Such time series have some distinct important characteristics, such as heavy tails and occasionally sudden large jumps....
The kramers–moyal coefficients of non-stationary time series and in the presence of microstructure (measurement) noise
, Article Understanding Complex Systems ; 2019 , Pages 181-189 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
Most real world time series have transient behaviours and are non-stationary. They exhibit different type of non-stationarities, such as trends, cycles, random-walking, and generally exhibit strong intermittency. Therefore local stochastic characteristics of time series, such as the drift and diffusion coefficients, as well as the jump rate and jump amplitude, will provide very important information for understanding and quantifying “real time” variability of time series. For diffusive processes the systems have a longer memory and a higher correlation time scale and, therefore, one expects the stochastic features of dynamics to change slowly. In contrast, a rapid change of dynamics with...
Distinguishing diffusive and jumpy behaviors in real-world time series
, Article Understanding Complex Systems ; 2019 , Pages 207-213 ; 18600832 (ISSN) ; Sharif University of Technology
Springer Verlag
2019
Abstract
Jumps are discontinuous variations in time series and with large amplitude can be considered as an extreme event. We expect the higher the jump activity to cause higher uncertainty in the stochastic behaviour of measured time series. Therefore, building statistical evidence to detect real jump seems of primary importance. In addition jump events can participate in the observed non-Gaussian feature of the increments’ (ramp up and ramp down) statistics of many time series [1]. This is the reason that most of the jump detection techniques are based on threshold values for differential of time series. There is not, however, a robust method for detection and characterisation of such discontinuous...