Sharif Digital Repository

In this thesis we are going to forecast the hourly consumption of the electricity over the country with two models and then, combine them. The first model decomposes the consumption to a deterministic trend and a stochastic residual. The second one assumes that the trend part is also stochastic.Once the consumption is being predicted separately by the models, in the second part of the thesis, we will combine the results to get a final prediction. This prediction is going to be compared with the load forecast of the Dispatching Unit of the electricity network as a base model. We are going to answer two important questions: firstly, does combining the models give a better prediction or not,... 

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... 

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  

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... 

The seasonal fluctuations in economics variables relate to the different behavior of economic agents across different seasons. In past, seasonality has been viewed as a redundant feature that needs to be removed from data before economic analysis. From 1988, modeling seasonality has become the major concern of many economists; moreover, it was seen that many economic analysis and forecasts could be flawed if seasonality is ignored. In the present research, periodic times series approach is used for the first time in modeling the seasonality feature of the housing market. Regarding the importance of the housing sector in economy from micro and macroeconomic points of view, using a more... 

Crop classification is one of the most important applications of remote sensing in agriculture. Knowing what crops are on the farm is invaluable both on a micro and macro scale. For example, this information can be used to design and imple- ment agricultural policies, product management and ensure food security. Also, this information can be used as a prerequisite for implementing other programs at the farm scale, such as monitoring and detecting anomalies during the crop growth cycle. Most of the studies in this field are focused on the optical data of the Sentinel-2 satel- lite, but the optical data are vulnerable to atmospheric conditions, and on the other hand, there is valuable... 

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... 

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... 

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... 

Real-time travel time estimation is a major requirement in many transportation related systems. One of the main challeges is to estimate the traffic speed and then forecast it for a short time. A valuable data source for this task is instant location of moving cars that is captured using global positioning system (GPS) and sent through internet in online manner. The main problem is that the resulting traffic data is severely sparse and also contains a lot of noise. Previous researchs on this type of data are mostly based on matrix or tensor factorization. In this work it is shown that despite the large fraction of missing value it is possible to use neural network for this problem with some... 

The importance of transfer learning in image-related problems comes from its many advantages that are sometimes undeniable. Previous researches have well shown the success of transfer learning in this area using deep neural networks. However, transfer learning for time series data has not yet been done in a conventional and automated manner. The main reason for avoiding transfer learning in this domain relates to the dynamic and stochastic nature of the time series, where they show a time-varying behavior. Previous experiments have shown that transfer learning between two heterogeneous time series could harm the forecasting accuracy of a model. Therefore, in this thesis, we aim to explore... 

An important class of applications which have been rapidly growing recently is the one that create and use time series data. These types of data sets are ordered based on the timestamps associated to their data items. In practice, traditional relational databases are unable to satisfy the requirements of these data sets; however, NoSQL databases with column-wide data structure are appropriate infrastructure for them. These databases are very efficient in read and write operations (especially for time series data, which are ordered) and are able to store unstructured data. Time series data may contain valuable and sensitive information; hence, they should be protected from the information... 

Nowadays, various policies are adopted by transportation managers and planners. These policies aim to improve system performance, reduce user costs, control and reduce air pollution, reduce noise pollution, and ultimately reduce congestion. A set of these policies in the form of transportation demand management is presented in the literature. A common way to find the effect of a policy on user behavior is to use questionnaires. Other causal inference models have been proposed in disciplines such as statistics, political science, marketing science, epidemiology, and psychology. The purpose of these models is to find the causal effect of an intervention (treatment) on a system. These studies... 

Water shortages resulting from macro-environmental climate changes as well as local inefficient agricultural practices and dam constructions activities have resulted in the gradual reduction of water level in Urmia Lake, located in the northwest of Iran. As such, restoration efforts were initiated to prevent further adverse impacts exacerbating the conditions and creating secondary problems such as regional salt dust generation and dispersion, resulting in health issues for the greater area population in the neighboring vicinities. The utilization of advanced forecast modeling based on deep learning algorithms can assist the authorities to manage better multi-dimensional issues affecting the... 

Human as an important part of the natural environment and can exert their positive or negative effects in the form of long-term and short-term on a natural environment. Human impacts on the natural system are very complex and consist of various components. The most considerable among them from past to now are maybe land use and land cover change, although, the impact of dam construction, water pollution, air pollution and etc, can not be neglected. To quantify the impact of these changes, many researchers have studied meteorological and climatic parameters using statistical relationships, but one major problem always existed, the low spatial accuracy of meteorological data. In recent years,... 

Tehran possesses various environmental crises due to excessive population growth, a huge increase of vehicles and heavy concentrated industries. One of the most important concern is air pollution. Spectral Analysis by discrete Fourier transform are described and applied to harmonic analysis of time series for detecting Present periodicities.
The current work proposes an approach for the determine the contribution of different frequencies to the data variance using air quality measured data. In this research, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. Because of The air quality data in Tehran have irregular sampling periods and... 

This study proposes a method to predict the remaining useful life (RUL) of the rolling element bearings (REBs) by forecasting the future trend of the peak of the acceleration signal. It is also employed to determine an appropriate time interval between the measurements of REBs vibration to reduce the error of forecasting and avoid collecting too much data in addition to increasing the reliability. In the first step, in order to achieve better results, the history of the acceleration peak is transformed into a stationary space before using the long short-term memory (LSTM) model to make it normally distributed and stationary. Then, LSTM forecasts the future trend of the stationary time series... 

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... 

Chaotic time series analysis, study of statistical behavior of chaotic maps and eventually an attempt to reconstruction and prediction of dynamical and statistical properties of output data of chaotic systems using stochastic models such as Markov models and autoregressive-moving average models are the main purposes of the present research. Examples of chaotic time series abound in the output of economics, engineering systems, the natural sciences (especially geophysics and meteorology) and social sciences. An intrinsic feature of an output time series of a dynamic system is that, adjacent observations are dependent. Time series analysis is concerned with techniques for the analysis of this... 

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...