Cell Phones Price Forecast Based on Adaptive Sliding Windows

Yonghua Yin Faculty of Computer Engineering Huaiyin Institute of Technology

Huaian, China 1165790450@qq.com

Jiajun Zong and Quanyin Zhu* Faculty of Computer Engineering Huaiyin Institute of Technology

Huaian, China hyitzqy@126.com

Abstract—Nowadays, the demand of people on the phones is very large. In order to help consumers have the better reference resources when they buy mobile phones, cell phones price forecasting on adaptive sliding windows is discussed in this paper. One year price for ten type’s mobile phone which extracted from www.360buy.com is used as the original data to forecast the price based on the adaptive sliding windows. According to this forecasting method, the experiments are implemented under the different sliding window width for different type’s cell phones depend on the accuracy rata. Comparing the experimental results with the original data, the forecast results are very satisfactory. And the forecasting average accuracy obtains 99.4 percent. Experiment results prove that the research is meaningful and useful and it is not only for consumers, but also for businesses in the cell phones market.

Keywords- price forecast; cell phones; linear backfilling; adaptive sliding windows

I. INTRODUCTION In recent years, the mobile phone market is developing

rapidly. On the one hand, the consumers have a huge demand of the cell phones and the mobile phone has become the essential goods of life. On the other hand, the manufacturer’s research and development speed is also very fast. Due to these two reasons, the price fluctuations have the great effect on the cell phones market. However, now most of the price forecasts are concentrated in the electricity prices, agricultural products, gold and the futures etc. According to this, cell phones price forecast is discussed in this paper. The original data used in this paper is the year of price data of ten different models of cell phones from www.360buy.com. And in the price forecast, the adaptive sliding windows of price forecast method is used. The experiments are implemented in this experimental environment and the experimental results show that cell phones price forecast based on adaptive sliding windows are very satisfactory.

II. SLIDING WINDOWS Definition 1: Set cycle time series in the time of observation period t is

x1, x2,…, xt, …,ft,1 is the predictive value of the next time t+1; ft,1= the latest forecast average =the average of xt,xt-1,…,xt-

N+1.N is given the parameters and it is the forecast window. N

determines the prediction accuracy. The experimental data are generally based on experience.

*Corresponding author. hyitzqy@126. Com Definition 2: xt is the actual value of the time t and ˆtx is the forecast

value of the time t. Prediction error:

ˆ , 1, 2, ,t t te x x t n= − = � (1) Relative prediction error:

ˆ

, 1,2, ,t t tt

t t

e x xe t nx x

−= = =� �

(2) Mean absolute error:

1 1

1 1 ˆn n

t t tt t

MAE e x xn n= =

= = −� � (3)

The mean absolute percentage error:

1 1

ˆ1 1n nt t t

t tt t

e x xMAPE

n x n x= =

−= =� � (4)

The predicted value of the next time: 1 2 ( 1)

1 ,1ˆ t t t t Nt t

x x x xx f

N− − − −

+

+ + + += =

� (5)

The average absolute error of the observation period t: ( )2

1

1 ˆt

t tt N

MSE x xt N − +

= −− � (6)

III. EXPERIMENTAL ENVIRONMENT In this paper, the adaptive sliding windows of price

forecast method is used. And the original data is the year of price data of ten different models of cell phones from www.360buy.com.

In this paper, improvements have made on the traditional sliding windows called as the adaptive sliding windows. In this experimental environment, the last seven data of the year of price data of each model of cell phones from www.360buy.com are used as the actual data compared to the forecast price. And the remaining data are used as the forecast samples to forecast prices.

And when forecast the first data, eighteen different sliding windows values are given. SW is seen as the sliding windows values. And the SW is given by equation (7).

(3, 4,5,..., 20)SW = (7)

*Corresponding author. hyitzqy@126. Com

2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science

978-0-7695-4818-0/12 $26.00 © 2012 IEEE

DOI 10.1109/DCABES.2012.20

247

Through the use of different sliding windows values, eighteen forecast values will be gotten. Yn is seen as the forecast values.

The value is selected from all the forecast value Yn of the minimum error as the final forecast value Y and the test sample of the next forecast.

SW is not changing when forecasting after next six data. Bsw is seen as the best SW. Bsw is gotten with the Bsw1, Bsw2 and Bsw3 coupled with a custom weight W. The Bsw1, Bsw2 and Bsw3 are the SW of the best three forecast errors when forecasting the first data. Bsw is given by equation (8).

1 1 2 2 3 3

1 2 3

* * *Bsw w Bsw w Bsw wBsww w w

+ +=+ +

(8) W is given by equation (9) and in this experiment

environment W=[2,4,2]. 1 2 3[ , , ]W w w w= (9)

When forecasting remaining data, every forecast value will be used as the test sample for the next forecast until the final data is gotten.

IV. EXPERIMENT WITH DATA The result of experiment with the year of price data of ten

different models of cell phones from www.360buy.com is shown in Table 1.

TABLE I. THE BEST AVERAGE ERROR OF THE TEN KINDS OF AGRICULTURAL PRODUCTS

Name ABE Name ABE

Sony Ericsson LT15I 0 Sony Ericsson MT15I 0.014

HTC T9199 0 HTC S710E 0.038MOTOROLA ME511 0 MOTOROLA ME722 0

NOKIA E66 0 NOKIA E72I 0 SAMSUNG S5670 0.015 SAMSUNG W609 0

In the Table 1, Name is the names of ten different models of cell phones. ABE is the average error of the seven best errors of each model of cell phone in the experiment with the original data.

Original data, forecast data and the best average errors of ten different models of cell phones are shown in Figure 1, Fig. 2 and Figure 3.In the Fig. 1 and Fig.2, X lable is the seven days and Y label is the CNY (China Yuan). In the Fig. 3, X lable is the seven days and Y label is the MAE (Mean Absolute Error).

As shown in the Fig. 3, the best average errors of ten different models of cell phones are very satisfactory compared to the original data. Analyzed the results gotten in this experimental environment, it can be seen that forecast price of cell phones with the adaptive sliding windows is useful.

The following figures from Fig. 4 to Fig. 13 show the original data and the best forecast data of the each model of ten type’s cell phones.

Figure 1. Original data of ten different models of cell phones

Figure 2. Forecast data of ten different models of cell phones

Figure 3. The best average errrors of ten different models of cell phones

Figure 4. The best price forecast and actual data of the Sony Ericsson LT15I

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Figure 5. The best price forecast and actual data of the Sony Ericsson MT15I

Figure 6. The best price forecast and actual data of the HTC T9199

Figure 7. The best price forecast and actual data of the HTC S710E

Figure 8. The best price forecast and actual data of the MOTOROLA ME511

Figure 9. The best price forecast and actual data of the MOTOROLA ME722

Figure 10. The best price forecast and actual data of the NOKIA E66

Figure 11. The best price forecast and actual data of the NOKIA E72I

Figure 12. The best price forecast and actual data of the SAMSUNG S5670

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Figure 13. The best price forecast and actual data of the SAMSUNG W609

CONCLUSION In this paper, seven days prices of ten different models of

cell phones are forecasted with the year of price data of each model of cell phones from www.360buy.com based on the adaptive sliding windows. And the results are ideal compared to the original data in this experimental environment. Whether this method being applicable to other experimental environment needs further exploration and further experimental validation, such as modifying custom weight and modify the data processing method.

ACKNOWLEDGMENT This work is supported by the National Sparking Plan

Project of China (2011GA690190), the fund of Huaian Industry Science and Technology. China (HAG2011052, HAG2011045 HAG2010066).

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