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التنبؤ في السلاسل الزمنية
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Added by
Damascus University حلقة بحث
Publication date
2018
fields
Informatics Engineering
and research's language is
العربية
Created by
FERAS K
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حظيت نمذجة وتوقع السلاسل الزمنية بأهمية كبيرة في العديد من المجالات التطبيقية كالتنبؤ بالطقس وأسعار العملات ومعدلات استهلاك الوقود والكهرباء، إن توقع السلاسل الزمنية من شأنه أن يزود المنظمات والشركات بالمعلومات الضرورية لاتخاذ القرارات الهامة، وبسبب أهمية هذا المجال من الناحية التطبيقية فإن الكثير من الأعمال البحثية التي جرت ضمنه خلال السنوات الماضية، إضافةً إلى العدد الكبير من النماذج والخوارزميات التي تم اقتراحها في أدب البحث العلمي والتي كان هدفها تحسين كل من الدقة والكفاءة في نمذجة وتوقع السلاسل الزمنية.
No English abstract
References used
Asha Farhath, Arputhamary, Arockiam, 2016, A Srvey On ARIMA Forecasting Using Time Series Model
Mahalakshmi, Sridevi, .Rajaram, 2016, A Survey on Forecasting of Time Series Data
Ouahilal Meryem, Jellouli Ismail, El Mohajir Mohammed, 2014, A Comparative Study Of Predictive Algorithms For Time Series Forecasting
Thomas Kolarik, Gottfried Rudorfer. 2015, Time Series Forecasting Using Neural Networks
Ratnadip Adhikari, R. K. Agrawal, 2013, An Introductory Study on Time Series Modeling and Forecasting Ratnadip
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The study aims at comparing ARIMA models and the exponential
smoothing method in forecasting. This study also highlights the special
and basic concepts of ARIMA model and the exponential smoothing
method.
The comparison focuses on the ability
of both methods to forecast
the time series with a narrow range of one point to another and the time
series with a long range of one point to another, and also on the different
lengths of the forecasting periods. Currency exchange rates of Shekel to
American dollar were used to make this comparison in the period
between 25/1/2010 to 22/10/2016. In addition, weekly gold prices were
considered in the period between 10/1/2010 to 23/10/2016. RMSE
standard was used in order to compare between both methods. In this
study, the researcher came up with the conclusion that ARIMA models
give a better forecasting for the time series with a long range of one point
to another and for long term forecasting, but cannot produce a better
forecasting for time series with a narrow range of one point to another as
in currency exchange prices.
On the contrary, exponential smoothing method can give better
forecasting for Exchange Rates that has a narrow range of one point to
another for its time series, while it cannot give better forecasting for long
term forecasting periods
The study and design of water dams depend essential on prediction of water volumes
or future predicted in rivers, by using the time series analysis of the historical
measurements.
The research aims to make statistical study of monthly water volume
s incoming in
AL-Aroos River in Syrian coastal and future prediction of these volumes. And the
Box-Jenkins models is adopt to analysis the time series data, because of its high accuracy.
We attend the monthly water volumes for 15 years. And after doing the wanted tests on
model residuals we found that the best model to represent the data is SARIMA(0,1,2)
(1,2,1)12 , and after dividing the data to 14 years to build the model and one year to test it ,
and depending on the smallest of weighted mean of criteria RMSE, MAP, MAE,. The best
predicted model is SARIMA (1,1,0) (0,1,1)12 and the model give the nearest predicted of
measured data actually.
We discussed in this work some predictive methods for time series and it is decomposing time series to its component (trend, Seasonality, cycle, random), Exponential smoothing, ARIMA, then we discussed some combining methods, then we formed a new c
ombine for predict time series which depends on combining exponential smoothing and ARIMA using weighted average with MAPE weights, and applied all methods above on three seasonal time series , first hourly temperature in Aleppo in august 2011 ,second monthly milk production peer cow in Australia from Jan 1962 to Dec 1975,third quartly electricity production in Australia from Mar 1956 to Sep 1994, and compared the results which approved that the suggested method is the best.
The study and design of water-intakes on springs is based on the analysis of time series of
historical measurements to achieve prediction of incoming water volumes or future
expected.
The research aims to model the monthly water flows of AL-SIN Sp
ring in Syrian Coast
and future expectations of these flows, by adopting the Box-Jenkins models to analyze the
time series data, due to its reliable accuracy. Monthly water flows, thus, monthly volumes,
for 101 month (from June 2008 to October 2016) were processed. Performing the stability
of the time series on variance and median and non-seasonality and making the wanted tests
on model residuals, we found that the best model to represent the data is SARIMA(2,0,1)
(2,1,0)12 , and after dividing the data into 81 month to build the model and 20 month to test
it. Depending on the smallest of weighted mean of criteria RMSE, MAP, MAE,. The best
predicted model was SARIMA (3,1,0) (1,1,0)12 and the model gave the nearest predicted
values to actually measured data in spring.
This study aimed to analyze the status of shares related to the
banking sector in Amman Stock Exchange, through the use of time series
analysis, relying on the achievement of the following objectives:
1- Analysis of the status of shares related to
the banking sector in
Amman Stock Exchange, through the use of time series analysis.
2- Access to an efficient market through the application of the
conditions existing in the market.
3- Analysis of the status of the general trend of stock prices in
Amman Stock Exchange, through the turnover rate of shares over
twelve months for eight years starting from 2000-2007 in order to
find the variables affecting performance.
4- Identifying the most important components of the time series
affecting stock prices in Amman Stock Exchange (seasonal,
periodical, and random), in addition to identifying which of these
components are responsible for stock price changes.
5- Trying to determine the general tendency of the time series of
stock prices for the coming period through the use of the model of
basic components.
This study was based on three major hypotheses. The study sample
consisted of the banks listed in Amman Stock Exchange with a total of 17
banks. Furthermore, Microsoft Office Excel had been used to analyze the
data of turnover rate of shares in Amman Stock Exchange to reach
conclusions.
The study came to a number of conclusions such as:
• Results showed that the influence of irregular variables on the
turnover rate of shares related to the banking sector, listed in
Amman Stock Exchange, was clear, in addition to the impact of
changes related to the general trend as well as the seasonal and
periodical changes.
• Results showed that the size of circulation plays a major role in
changing the direction of prices. Thus, in the case of higher prices,
increased circulation is desired, while decreased circulation will be
the case for low prices.
Based on the above conclusions, the researcher presented a series
of suitable recommendations for the use of analysis model of time series
in analyzing the status of shares in Amman Stock Exchange.
suggested questions
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