The general index of the financial market of the important
economic indicators in any country is being reflects the
economic situation and economic activity in the country, so
attention must be appropriate methods for predicting the
performance o
f this indicator in the future and look at the
factors that affect in it .
This study aimed to the conclusion based, follow Box-Jenkins
methodology for building predictive models ARMA (p, q) and
check models" residuals, and predict the performance of the
general index of Damascus Securities Exchange DWX, as
well as the volume of trading in this market, and studying the
impact of the relationship between them .
We performed in this research forecast in the direction of the index
numbers for consumer prices for ( food- clothes and shoes –
education -health- transportation communications - housing water,
electricity, gas and other fuel oils), by using Mark
ov chains in
estimating with dependence on monthly data were taken from the
central bureau of statistics in Syria during the period (1/1/2010 ,
31/12/2011) , So results were analyzed by calculating the vector of
states probabilities in the moment 0 t and using it with matrix of
transition probabilities states transition probability for forecasting in the
vector of states probabilities on the long and short range for knowing
the direction at which the index numbers may behave in the future.
The most important results of the study were instability of the beam of
the transition probabilities (high low stability) during the prediction
period, as well as for the matrix of transition probabilities.
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.
Given a heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present
TENSORCAST, a novel method that forecasts time-evolving networks more accurately than the current state of the art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with a different structure. We run our method on multiple real-world networks, including DBLP and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.
