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S&P500 Intelligent Trading Using Neural Networks
,
M.Sc. Thesis
Sharif University of Technology
;
Akhavan Niaki, Taghi
(Supervisor)
Abstract
This project tries to select the inputs which really affect the change in the direction of S&P500. For this purpose, design of experiments and analysis of variance are used. T tests are carried out to calculate the statistical significance of mean differences. Experiment results indicate that the designed neural networks with the selected inputs significantly outperform the traditional logit model with respect of the number of correct predictions. Moreover, real trades are simulated using the neural network predictions in the test period and the results show that using the designed neural network can significantly increase the income.
Change Point Estimation for Multistage Processes
, Ph.D. Dissertation Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
Knowing the time of change would narrow the search to find and identify the variables disturbing a process. Having this information, an appropriate corrective action could be implemented and valuable time could be saved. Multistage processes that are often observed in current manufacturing processes must be monitored to assure quality products. The change-point detection of such processes has not been proposes investigated yet. Thus, this dissertation proposes maximum likelihood step-change estimators of two kinds of these processes. First, a multistage process with variable quality characteristics is considered and formulated by the first-order auto-regressive model. For the location...
Improved Supply Chain Management Performance by Applying Hybrid Forecast Method
, M.Sc. Thesis Sharif University of Technology ; Hajji, Alireza (Supervisor)
Abstract
In today’s competitive world, using an efficient forecast method is necessity for companies. To now, many forecast methods have been developed, but many of them have not an expected efficiency. In this research, we develop a new hybrid forecast method with application of forecasting retails demand. The hybrid method is the combination of ARIMA method and neural networks. To test the efficiency of the method we use the 96 weeks data of plastic containers demand. We also comprise the hybrid method with other forecast methods including naïve method, ARIMA method and neural network method by applying root mean square error and mean absolute percentage error indexes. In the case of plastic...
Fault Growth Forecasting of Rotatory Systems Using Wavelet Transform and Artificial Neural Network Algorithm
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mahdi (Supervisor) ; Mahdigholi, Hamid (Supervisor)
Abstract
Failure of mechanical parts in the industry lead to a larger system downtime and even imposing economic losses to the factory. For this Purpose, for many years, researchers have been trying to find ways to predict early failure and to prevent losses from occurring. Creation of new sciences like artificial intelligence, helped researchers in this field.In the current study, using experimental data of a set of bearings that have been tested and recorded in the Intelligent Systems Research Center, A new approach with sufficient accuracy is presented for the prediction algorithm. Among the features extracted, three features of entropy, root mean square and maximum are the most appropriate...
Chaos in the APFM nonlinear adaptive filter
, Article Signal Processing ; Volume 89, Issue 5 , 2009 , Pages 697-702 ; 01651684 (ISSN) ; Haeri, M ; Sharif University of Technology
2009
Abstract
In this paper, we show that an amplitude phase frequency model (APFM) which is used as a nonlinear adaptive filter in signal processing can exhibit chaotic behavior. It is illustrated that both frequency and amplitude of the input components have effect on the filter characteristics which can lead the filter to behave chaotically for some values of these parameters. We have exploited different measures such as the Lyapunov exponents, bifurcation diagram, sensitivity to initial condition, and 0-1 test on time series to confirm our claim. Existence of the chaotic behavior in APFM does not conform to the prior conjectures about this filter. © 2008 Elsevier B.V. All rights reserved
Forecasting P/E Ratio by Decomposing into Constituent Factros
, M.Sc. Thesis Sharif University of Technology ; Zamani, Shiva (Supervisor) ; Abdoh Tabrizi, Hossein (Supervisor)
Abstract
P/E ratio is studied in four levels in this study:
1)Macroeconomics level
2)Capital market level
3)Industry level
4)Company level
The first level studies effects of macroeconomics variables on P/E ratio. At this level we use variables such as economic growth, inflation, exchange rate, and etc.The next level uses capital market variables such as market volume, and IPO information.The third level that we study in this research is industry level. Stocks of an industry generally behave similar, because they have common advantages and disadvantages, thus industry is an effective factor on P/E ratio.The last level studies financial statements and internal features of a...
1)Macroeconomics level
2)Capital market level
3)Industry level
4)Company level
The first level studies effects of macroeconomics variables on P/E ratio. At this level we use variables such as economic growth, inflation, exchange rate, and etc.The next level uses capital market variables such as market volume, and IPO information.The third level that we study in this research is industry level. Stocks of an industry generally behave similar, because they have common advantages and disadvantages, thus industry is an effective factor on P/E ratio.The last level studies financial statements and internal features of a...
Forecasting P/E Ratio Using Neural Networks
, M.Sc. Thesis Sharif University of Technology ; ahramgiri, Mohsen (Supervisor)
Abstract
This thesis firstly studies the parameters affecting P/E ratio. These parameters vary from Macroeconomics level, Economic growth and Inflation, to company level. Then this study deploys Neural Networks to predict magnitude of P/E and change direction of P/E ratio. To increase accuracy, thesis uses three different method of normalizing for Input data. Finally, results are compared to results of regression method
Design of Sensory Gove for Recognition of Persian Sign Language
, M.Sc. Thesis Sharif University of Technology ; Vossoughi, Gholamreza (Supervisor) ; Parnianpour, Mohammad (Supervisor)
Abstract
Sign language is recognized by considering the combination of the hand gesture, orientation, location and patterns of hand and arm movement. These complex interactions make the character and word recognition very challenging. In this paper, with the aid of sensory gloves and multiple inertial measurement units (IMUs) we measure the shoulder and elbow joint trajectories and hand gesture to train the discriminant functions to recognize the words intended and represented by sign language. For recognition of hand gesture, the Naive Baysian classifier was used while for Eulerian angles a new time series similarity measures were computed. Different aggregation method was used to integrate temporal...
Determining the Optimal Level of Reserve in Power Systems with High Penetration of Wind Energy
, M.Sc. Thesis Sharif University of Technology ; Abbaspour Tehrani Fard, Ali (Supervisor) ; Fotuhi-Firuzabad, Mahmud (Co-Advisor)
Abstract
Shortage in fossil fuels resources together with the pollution concerns have caused a systematic change in power system planners and decision makers policies to renewable energies as an alternative to produce electrical energy. However, some intrinsic features of the wind energy overshadow its profitability. Inability to predict the wind speed changes and consequently the output level of wind turbines, being an uncontrollable generation unit, and also being an intermittent unit can be accounted as the main attributes of renewable-based units. Taking into account these features, one can conclude that new challenges can be brought into existence in planning and operation issues of...
Analysis and data-driven reconstruction of bivariate jump-diffusion processes
, Article Physical Review E ; Volume 100, Issue 6 , 2019 ; 24700045 (ISSN) ; Heysel, J ; Lehnertz, K ; Rahimi Tabar, M. R ; Sharif University of Technology
American Physical Society
2019
Abstract
We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion and two-dimensional jumps, that can be coupled to one another. We present a data-driven, nonparametric estimation procedure of higher-order (up to 8) Kramers-Moyal coefficients that allows one to reconstruct relevant aspects of the underlying jump-diffusion processes and to recover the underlying parameters. The procedure is validated with numerically integrated data using synthetic bivariate time series from continuous and discontinuous processes. We further evaluate the possibility of estimating the parameters of the jump-diffusion model via data-driven analyses of the higher-order Kramers-Moyal...
Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters
, Article Annals of Nuclear Energy ; Volume 132 , 2019 , Pages 87-99 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, some important operating parameters of nuclear power plants (NPPs) transients are forecasted using different supervised learning methods including feed-forward back propagation (FFBP) neural networks such as cascade feed-forward neural network (CFFNN), statistical methods such as support vector regression (SVR), and localized networks such as radial basis network (RBN). Different learning algorithms, including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are used in CFFNN method. SVR method is used with different kernel functions including Gaussian, polynomial, and...
On the existence of proper stochastic Markov models for statistical reconstruction and prediction of chaotic time series
, Article Chaos, Solitons and Fractals ; Volume 123 , 2019 , Pages 373-382 ; 09600779 (ISSN) ; Salarieh, H ; Alasty, A ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, the problem of statistical reconstruction and prediction of chaotic systems with unknown governing equations using stochastic Markov models is investigated. Using the time series of only one measurable state, an algorithm is proposed to design any orders of Markov models and the approach is state transition matrix extraction. Using this modeling, two goals are followed: first, using the time series, statistical reconstruction is performed through which the probability density and conditional probability density functions are reconstructed; and second, prediction is performed. For this problem, some estimators are required and here the maximum likelihood and the conditional...
Multifractal detrended fluctuation analysis of continuous neural time series in primate visual cortex
, Article Journal of Neuroscience Methods ; Volume 312 , 2019 , Pages 84-92 ; 01650270 (ISSN) ; Bahadorian, M ; Doostmohammadi, J ; Davoodnia, V ; Khodadadian, S ; Lashgari, R ; Sharif University of Technology
Elsevier B.V
2019
Abstract
Background: Local field potential (LFP) recordings have become an important tool to study the activity of populations of neurons. The functional activity of LFPs is usually compared with the activity of neighboring single spike neurons with sampling rates much higher than those of the continuous field potential channel (5 kHz). However, comparison of these signals generated with the lower sampling rate technique is important. New method: In this study, we provide an analysis of extracellular field potential time series using the sophisticated nonlinear multifractal detrended fluctuation analysis (MF-DFA). Using the MF-DFA, we demonstrate that the integral of the singularity spectrum is a...
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