scholarly journals A Type of Combination Forecasting Method Based on Time Series Method and PLS

2012 ◽  
Vol 02 (04) ◽  
pp. 467-472 ◽  
Author(s):  
Liang Wan ◽  
Biao Luo ◽  
Hong-Mei Ji ◽  
Wei-Wei Yan
Keyword(s):  
Time Series ◽  
Series Method ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Time Series Method

Comparison Analysis of Aircraft Aluminum Alloy Corrosion Damage Forecasting Method

2012 ◽  
Vol 268-270 ◽  
pp. 348-351
Author(s):  
Zhi Guo Liu ◽  
Zhi Tao Mu ◽  
Zeng Jie Cai
Keyword(s):  
Time Series ◽  
Aluminum Alloy ◽  
Corrosion Damage ◽  
Neural Net ◽  
Series Method ◽  
Comparison Analysis ◽  
Forecasting Accuracy ◽  
Fitting Method ◽  
Forecasting Method ◽  
Time Series Method

Three different analysis methods was put forward to carried out aircraft aluminum alloy structure corrosion damage forecasting,and comparison analysis of different method which included basic forecasting caculation principle and forecasting accuracy and forecasting extensionality also was discussed.The forecasting calculation result shows that the prediction accuracy of neural net and time series method is higher than the data fitting method,and the prediction extensionality of time series method is the best among the three method which discussed.

scholarly journals PERAMALAN PERSEDIAAN INFUS MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA RUMAH SAKIT UMUM PUSAT SANGLAH

2018 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
I PUTU YUDI PRABHADIKA ◽  
NI KETUT TARI TASTRAWATI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Time Series ◽  
General Hospital ◽  
Moving Average ◽  
Glucose Infusion ◽  
Series Method ◽  
Autoregressive Integrated Moving Average ◽  
Important Thing ◽  
Forecasting Method ◽  
Time Series Method ◽  
The Many

Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indicate the need for infusion at Sanglah Hospital as many as 154,831 units for infusion of 0.9% NaCl 500 ml and 8,249 units for 5% 500 ml Glucose infusion.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

Waveform computations by the time-series method

1952 ◽  
Vol 1952 (10) ◽  
pp. 246-246 ◽  
Author(s):  
N.W. Lewis
Keyword(s):  
Time Series ◽  
Series Method ◽  
Time Series Method

scholarly journals IMPLEMENTASI METODE FUZZY TIME SERIES DENGAN MODEL ALGORITMA CHEN UNTUK MEMPREDIKSI HARGA EMAS

2020 ◽  
Vol 13 (1) ◽  
pp. 71-78
Author(s):  
Darsono Nababan ◽  
Eric Alexander
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Gold Price ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Series Method ◽  
Market Uncertainty ◽  
Low Interest Rates ◽  
Calculation Results ◽  
Time Series Method

Gold is one of the people's preferred forms of investment and is considered the safest (save -heaven). Gold risk which is considered small is the main attraction because in general Indonesian people are not yet familiar with capital market investments such as stocks and mutual funds. But the price of gold is very volatile as for the factors that affect the fluctuations of gold are consumption demand, volatility and market uncertainty, protection of low-interest rates, and the US dollar. Predicting the movement of the gold price and knowing where the direction of the exchange rate moves and determining the price of gold up or down cannot be done accurately and consistently. For this reason, in reducing the risk of loss, an application is needed to predict gold prices using the Fuzzy Time Series Chen algorithm using MATLAB software. In this study to obtain prediction results and comparison charts using actual data and prediction data for the 2015-2017 gold price. From the calculation results obtained by the prediction results with the Fuzzy Time Series method with the Chen algorithm where the average difference between the actual data and prediction data is not more than Rp. 2,850, - where predictions using the Fuzzy Time Series method Chen's algorithm is sufficient to use 1 data to predict the second data which makes this method accurate in predicting the price of gold.

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