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Register a new userComponents of Temperature Time Series at Tartous meteostation
مكونات السلسلة الزمنية لدرجات الحرارة في محطة طرطوس
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ربا علي( باحث )
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تمت دراسة المركبات الأربعة لظاهرة درجة الحرارة (الصغرى و العظمى) و هي الموسمية (S) و النزعة أو الاتجاه العام (T) و الدورية (C) و العشوائية (I) لمدينة طرطوس. و استعملت أربع طرائق مختلفة (طريقة متوسط النسب المئوية، طريقة النسبة المئوية للاتجاه العام، طريقة النسبة للمتوسط المتحرك، طريقة الوصلات النسبية) في طرح المركبة الموسمية و من ثمّ تحديد الدليل الموسمي. فبيّن الاستنتاج الإحصائي بأن طريقة متوسط النسب المئوية يمكن استعمالها في التنبؤ بدرجة الحرارة لسنة 2003 اعتماداً على السجل التاريخي للفترة (1957-2002). و بعد طرح مركبات الاتجاه العام و الموسمية و العشوائية ظهرت مركبة الدورية واضحة في هذه الظاهرة. ثم تمّ تطبيق بعض الفحوصات الإحصائية المعروفة مثل المعدّل، و الانحراف المعياري، و الاحتمالية المتجمعة التي بيّنت وجود تطابق ما بين القيم التاريخية و المتنبأ بها.
The four components of temperature (max., min.) phenomena, seasonal(S), Trend(T), cyclical(C), and random (I) for Tartous city have been studied. Four different methods (Average percentages method, Percentage of the general trend method, The ratio of the moving average method, Link Relative method) are used to deduct the (S) components and seasonal index for each method is determined. The statistical inferences pointed that the Average percentages method can be used in the prediction of temperature. for the year 2003 depending on a historical record (1957- 2002). The result of this deduction showed that the temperature is a cyclical phenomena. The known statistical test like mean, Standard deviation and cumulative probability have been done which showed a good correlation between the predicted and historical data.
References used
MURRAY, R.S. Statistics. Mc Graw-Hill International book company, Newyork, 1972, 538
KEITH, W.H. Time Series Analysis in Water Resources. American water Resources Association, Canada, 1985, 609- 623
MUTREJA, K.N. Applied Hydrology. Tata Mc Graw-Hill company, New Dalhi, 1986, 959
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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.
The experiment was done within -2014 in plant physiology lab.of agriculture faculty
of Tishreen University for estimation of high temperature stress toleration of citrus leaves
in three varietis(Citrus Unshiu , Meyer Lemon,Citrus SinensisWashington
). Many random
samples of leaves were gathered from the foliage of the examined categories whereas tha
average of the taken leaves was 200 from the whole sides for each one tree from the
branches which are one year old . which were put for limited duration in a water bath about
many different degrees of temperature then they were put in water and then in a solution of
HCL acid .
Many references were studied of containing :non spotting on leaves ,which means
they are un harmed,simple spotting,the spotting of more than half of the leaves area,the
perfect leaves spotting.
Studied temperature were as the following :40-50-60-70-80.Each one of this
mentiond temperature,40 leaves were used .The statistics analys were done with the way of
Genstat 12,for the comparison between averages for the sake of calculating the least
significant difference at the guidance level :5% .
Studies showed that Satsuma leaves are the most toleration for high temperature,next
Citrus SinensisWashington is less than Satsuma.At last Meyer Lemon was the least .
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 analysis of time series data is one of the most important statistical
topics, usually focuses on forecasting the future behavior of the series at a
certain time for certain purposes.
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
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