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International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-7 Issue-5S, January 2019
251v
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication
Retrieval Number: ES2152017519/19©BEIESP
Forecasting Prices of Fish and Vegetable using
Web Scraped Price Micro Data
Mazliana Mustapa, Raja Rajeswari Ponnusamy, Ho Ming Kang
Abstract: In Malaysia, price statistics that are used as a proxy
for inflation is the Consumer Price Index (CPI). The web
scraped data has the possibility to become new source of
compiling the CPI. The benefits using the web scraped data is
can get the price information on a daily basis as compared to
traditional data collection which takes on weekly or monthly
basis. Price movement of the web scraped data can be monitored
in real time and can benefits to policy makers. Forecasting price
using the web scraped data helps the official statistics office to
predict future value and can be used to control the situation of
supply and demand side. Forecasting using web scraped data
allow the policy makers to make the quick and right decision at
the right time. Numerous studies have been conducted by the
other National Statistics Office regarding the web scraped data,
however studies on forecasting using web scraped is deficient.
Thus, this study aims to utilize the web scraped data in
forecasting ten selected fish and vegetables in Malaysia using
Auto Regressive Integrated Moving Average (ARIMA) approach.
The main objective of this study is to explore and evaluate the
dependability of the alternative online data prices to forecast
using ARIMA approach. The outcome of this research wills
benefits to the Department of Statistics, Malaysia (DOSM). The
forecasting model will be used to forecast price in the CPI
compilation. This information offers better estimation and more
timely. The modernization of the data collection by using the web
scraped data will helps to reduce the burden of the
establishments/supermarkets/wet markets. The coverage of CPI
will be extended and will produce good quality statistics. The
forecasting using web scraped data will improve understanding
or perception of price behavior. Price forecasting will be an input
to the policy makers when the price is increasing.
Keywords: Consumer Price Index, Web scraped data,
Forecasting, ARIMA
I. INTRODUCTION
The Consumer Price Index (CPI) measures the percentage
change purchasing cost by the residents of Malaysia in
“fixed basket” of goods and services over a specific time
period. There are 12 groups in CPI in order to calculate
price index, namely Clothing and Footwear; Housing,
Water, Electricity, Gas and Other Fuels; Health; Education;
Communication; Food and Non-alcoholic Beverages;
Restaurants and Hotels, Recreation Services and Culture;
Alcoholic Beverages and Tobacco; Transport;
Miscellaneous Goods and Services; and Furnishings,
Household Equipment and Routine Household Maintenance
(Department of Statistics, Malaysia, 2017).
Revised Manuscript Received on January 19, 2019.
Mazliana Mustapa is working in School of Computing, Asia Pacific
University of Technology and Innovation Malaysia .
Raja Rajeswari Ponnusamy is working in School of Mathematics,
Actuaries and Quantitative Studies, Asia Pacific University of Technology
and Innovation, Malaysia.
Ho Ming Kang is working in School of Mathematics, Actuaries and
Quantitative Studies, Asia Pacific University of Technology and
Innovation, Malaysia .
Official price statistics in Malaysia which are used to
measure for inflation is CPI. The CPI is published monthly
and it was released by the Department of Statistics,
Malaysia (DOSM) after conducting monthly price data
collection. The method of data collection is by visiting the
supermarkets or outlets which a quite conventional. The
monthly price data collection was done by the DOSM field
officers, which they are collected the price information by
visiting the supermarkets, wet markets or company of 14
states in Malaysia consists of urban and rural. The price data
collection is based on selected products and services in the
“fixed baskets”. The frequency of perishable products such
as fish and vegetables is weekly. Meanwhile, the monthly
price collection was done for the remaining products or
services.
E-commerce website is growing with the trends of
consumers purchase online. The e-commerce is in line with
tools and applications, which make consumer easy to
purchase online. Thus, e-commerce website has a rich
information about price. E-commerce web site has impacted
on big data, which allows us to have a potential new data
source. We can get more insights about the patterns of web
scraped data. The online retailer will provide pricing
information on their website on a daily basis. In this era,
consumers like to purchase goods through the online
channel.
As explained by Cavallo and Rigobon (2016), online price
offers many benefits such as real-time, the frequency of data
is daily basis, economical, full of information, easy to
collect anywhere and comparable in the country. Online
inflation statistics are used in Argentina using web data
begins when there is a discrepancy of inflation rate between
National Statistic Office of Argentina. The inflation rate
which is published by the National Statistic Office
Argentina did not represent the actual changes of price. The
online inflation rate is 20%, meanwhile the real inflation
rate is 8%. Online inflation rate shows the best actual
inflation rate and in line with the local economist
estimations and several agencies. Thus, the important of
online price inflation or index is needed and the index has
released every day since March 2008. The author also
mentioned the other elements features of online price data
which is price level information.
Web scrapping is the process of getting information from
the website using special tools named web scraper. The web
scraped data has the possibility to become new source of
compiling the CPI. The benefits using the web scraped data
is we can get the price information on a daily basis as
compared to traditional data collection
Forecasting Prices of Fish and Vegetable using Web Scraped Price Micro Data
252
Published By:
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& Sciences Publication
Retrieval Number: ES2152017519/19©BEIESP
which takes on weekly or monthly basis. Price movement
of the web scraped data can be monitored in real time and
can benefits to policy makers. Forecasting price using the
web scraped data helps the official statistics office to predict
future value and can be used to control the situation of
supply and demand side. Forecasting using web scraped
data allow the policy makers to make the quick and right
decision at the right time. Based on the ideas of Polidoro et
al. (2015), web scraped data on selected items such as
electronic products and airlines ticket are tested. The Italian
National Statistical Institute (ISTAT) did the research with
the main objective to have an alternative data source
through online data collection. The outcome of this
research shows good results where the process of data
collection becomes efficient. Using the web scraped data
helps decrease of the error of measurement and sampling
error. The web scraping technique presents coverage error
as neutral.Mayhew (2016) highlighted on the missing value
of web scraped data by applying the imputation technique.
Web scraped data has a few problems; scraper issue and the
items is not available on the website. The author stated a
simulation study is conducted in order to decrease the price
imputation bias. There are three techniques were applied in
the study, firstly computes the item or class mean, then
using the previous price and imputation ratio. The results of
the simulation study, the geometric average growth is
recommended to impute price as the data is part of CPI. The
author recommends the number of days for the imputation
of the price. At least, three days is the number of days that
can do price imputation before it is being eliminated from
the dataset. The effects of applying the price imputation are
the time series is smooth, regular and identical. Based on the
results, carry forward previous price is the best method of
price imputation with small bias.Metcalfe et al. (2016)
found the Clustering Large dataset into Price Indices
(CLIP), the statistical method. The CLIP method is to
produce price index with web scraped data. The author
mentioned that CLIP is a research index, which not suitable
in analysing the economy. The assumption in CLIP model is
that the consumer will buy a different product. Therefore, it
only caters for the price change of cluster and group of
services and goods. CLIP has a limitation where it depends
on the product availability.
Powell et al. (2017) discussed on the average prices of
web scraped price data of 33 food items of CPI products.
The author describes that the analysis has two datasets
which are 33 products of web scraped price of 14 months.
The supermarket policy in United Kingdom stated that they
have the same price across the country. Out of CPI weights,
web scraped data contribute 13%. The second dataset is
obtained from the CPI disaggregated data which are at the
price level. The new model is develops to address issues of
web scraped data. Hyper parameters estimates are produced
to tackle the different product prices. The estimation helps
to determine the products which can influence to CPI and
determine the web scraped price data which has a
correlation with the normal price collection survey.
Cavallo (2013) outlined the online price index, which is a
mixture of official methods and web price data. The author
claims that the online price index is equivalent to official
price statistics (inflation) in Colombia, Venezuela,
Argentina and Brazil.Chuanyang and Joseph (2016) stated
Singapore Department of Statistics has a pilot work of
collecting price information through web scraping. There
are two types of web crawler, “point-and-click” and
customized. Two issues were discussed along the pilot work
which are no consistency of product type and lack of staff,
which do not have the capability to handle the web
crawler.Breton et al., (2016) highlights the work of scraping
price from three websites in United Kingdom namely
Waitrose, Tesco and Sainsbury. About 6,500 price quotation
is collected on a daily basis of 35 CPI products within 13
months. The Gini, Eltetö, Köves and Szulc (GEKS) index is
proposed to overcome the chain issue. The GEKS is better
than the unit price index.
In Malaysia, official price statistics, namely CPI was
released three weeks after the calendar month. For example,
CPI December 2017 is published on January 24, 2018. CPI
December 2017 increased by 3.5 percent as compared to the
same month of the previous year. Food and Non-alcoholic
beverages is the second main group shows an increase of
4.1%. A time lag of the publishing CPI has influenced
decision to the policy makers or researchers on goods and
services inflation. Currently, food price, especially fish and
vegetables have become a hot issue after the government
implemented subsidy rationalization. Numerous studies
have been conducted by the other National Statistics Office
regarding the web scraped data, however studies on
forecasting using web scraped is deficient. Thus, this study
aims to utilize the web scraped data in forecasting ten
selected fish and vegetables in Malaysia using ARIMA
approach.
The main aim of this study is to utilize the web scraped
price data as an alternative data source and to forecast or
predict the selected fish and vegetables using time series
forecasting technique. The main objective of this study is to
explore and evaluate the dependability of the alternative
online data prices to forecast using ARIMA approach. There
are three specific objectives in this study. The first objective
is to determine the ARIMA model of the ten selected fish
and vegetables. The second objective is to forecast prices of
the selected fish and vegetables and finally the third
objective is to identify the best ARIMA model based on
performance measures.
II. METHODS AND MATERIALS
For the short term forecasting daily fish and vegetables,
an ARIMA forecasting technique was implemented. This
study applied for the ten selected fish and vegetables
namely, bawal, kembong, selar kuning, cencaru, red
bream, kangkung, round cabbage, long beans, green
spinach and sawi jepun. The time series frame is built for
selected of fish and vegetables, ten time series frame was
built from 1 July to 30 November 2017 (5 months of daily
price data). The imputation process of the missing value was
based on three methods which are taking the mean, median
and previous price.
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-7 Issue-5S, January 2019
253v
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication
Retrieval Number: ES2152017519/19©BEIESP
The ARIMA is the forecasting technique that has high
accuracy on forecast based on past value. ARIMA is applied
to non-stationary time series with ARIMA (p, d, q).
Michinaka et al., (2016) researched on price forecasting of
Japanese logs using ARIMA and exponential smoothing
method (ETS). The study is conducted on three Japanese
logs which are karamatsu, hinoki and sugi. The aim of the
research is to forecast monthly price of the selected logs of 6
and 12 months ahead. Based on the accuracy, ARIMA is
outperformed as compare to ETS method. Paul, Hoque and
Rahman, (2013) identified the best ARIMA model for daily
share price. The Square Pharmaceuticals Limited (SPL)
share price is used in this research. The author claims that
the serial data is not stationary even though it has
transformed to log price. Differencing the time series has
overcome the stationary issue. The best ARIMA model is
determine using the AME, RMSE, AIC, MAPE, SIC, AICc
based on least value of the measures. The best model for
forecasting share price is ARIMA (2, 1, 2). Abdullah (2012)
described that ARIMA models have the capability to present
many types of time series with the flexibility criteria. The
researcher stated that the objective of the ARIMA model to
predict the price of gold bullion coin (selling). The study
undertaken is developed based on pre and post period of the
forecast. The ARIMA (2, 1, 2) model is outperformed with
the minimum error 10%. It can conclude that the model is
good with the highest accuracy in predicting gold bullion
coin.
R software is chosen as the tools in this study, considering
a few factors. R Studio is the open source and free of data
analysis. Three libraries or packages were used when
performing the time series analysis namely „tseries‟,
„auto.arima‟ and „forecast‟. Two statistical test was
performed using the Box.test() function for Ljung Box test
and adf.test() function for Augmented Dickey Fulley
test.The time series which is stationary must meet the three
condition, mean is equal to zero, variance constant and
covariance is stabilize. The Augmented Dickey Fuller test is
to verify the time series is non-stationary or stationary. The
null and alternative hypothesis is outlined as
Ho : The series is not stationary and
H1 : The series is stationary.
The P-value is obtained automatically using the software
as 0.05 (95% confidence interval). If the p-value of ADF
test is less than 0.05, Ho is rejected.
Model adequacy is checked by applying a statistical test
which is Ljung-Box test. The hypothesis of Ljung-Box is
outlined as
H0: autocorrelations of chosen lags = 0 and
H1: autocorrelations of chosen lags ≠ 0.
The null hypothesis was rejected if the p-value is less than
0.05 (level of significance). Thus, one of the autocorrelation
is not zero. If the p-value is greater than 0.05, we accept the
null hypothesis with the assumption there is no
autocorrelation of the chosen lags. The accuracy of the
model is measured using Root Mean Squared Error
(RMSE), Mean Absolute Error (MAE) and Mean Absolute
Percentage Error (MAPE). This performance measure is
used to calculate the forecast error (et). The best forecast
error should be least value to identify the good fit model.
III. RESULTS
Table 1 shows the results of the Augmented Dickey-
Fulley test. Based on the results, all ten selected fish and
vegetables not stationary, referring to p-value which are
greater than level of significance, 0.05.
Table 1: Augmented Dickey Fuller (ADF) Test
Item
Original Time Series
First Difference
Dickey
Fuller
Lag Order
p-value
Dickey
Fuller
Lag Order
p-value
Bawal
-2.9603
35
0.1762 **
-5.5360
5
0.01 *
Cencaru
-2.2159
35
0.4864 **
-7.6904
5
0.01 *
Kembong
-2.3993
5
0.41 **
-6.6710
5
0.01 *
Red Bream
-3.1999
37
0.09052 **
-6.4815
5
0.01 *
Selar Kuning
-2.758
35
0.2605 **
-5.8303
5
0.01 *
Green Spinach
-2.2854
15
0.4574 **
-5.2735
5
0.01 *
Kangkung
-3.0936
36
0.1206 **
-5.0658
5
0.01 *
Long Beans
-3.284
10
0.07652 **
6.2167
5
0.01 *
Round Cabbage
-2.8829
10
0.2084 **
-5.8983
5
0.01 *
Sawi Jepun
-2.5199
10
0.3597 **
-6.3604
5
0.01 *
Note: ** denotes not significant at 0.05 level of significance, indicates that time series is notstationary by accepting the Null
Hypothesis
* the series is significant at 5% level, null hypothesis is rejected, Hence the series is stationary
Forecasting Prices of Fish and Vegetable using Web Scraped Price Micro Data
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Table 2 depicted the best ARIMA model by using
auto.arima function in R. From the results, it shows that
Bawal, Cencaru, Kembong, Round Cabbage and Sawi Jepun
has ARIMA (0, 1, 0) which is ARMA (0,0) if the difference
is removed. This situation is called Random Walk or White
Noise. Meanwhile, the other remaining items such as Red
Bream, Selar Kuning, Green Spinach and Kangkung has
ARIMA (2,0,1) with non-zero mean. Round Cabbage has
ARIMA model with ARIMA (1,0,2) without differencing
the series.
Table 2: ARIMA model
Item
ARIMA (p,d,q)
Bawal
ARIMA (0, 1 ,0)
Cencaru
ARIMA (0, 1 ,0)
Kembong
ARIMA (0, 1 ,0)
Red Bream
ARIMA (2, 0 ,1) with non-zero mean
Selar Kuning
ARIMA (2, 0 ,1) with non-zero mean
Green Spinach
ARIMA (2, 0 ,1) with non-zero mean
Kangkung
ARIMA (2, 0 ,1) with non-zero mean
Long Beans
ARIMA (1, 0 , 2) with non-zero mean
Round Cabbage
ARIMA (0, 1 ,0)
Sawi Jepun
ARIMA (0, 1 ,0)
The best ARIMA models are tabulated in Table 3. The model is developed using the best ARIMA fit model for the selected
fish and vegetables. Bawal, Cencaru, Kembong, Round Cabbage and Sawi Jepun are using the same model as seen in Table
3 below. Table 3: ARIMA best model
Item
Model
Red Bream
Yt= 2.4205 -0.0333Yt−1+ 0.6382Yt−2 +0.0594+0.9688et−1
Selar Kuning
Yt= 1.4794 -1.6648Yt−1 - 0.7431 Yt−2 + et - 0.7924et−1
Green Spinach
Yt= 1.1813 + 1.7246Yt−1 - 0.7774 Yt−2 + 0.1008 - 0.8126et−1
Kangkung
Yt=1.0995 + 1.7293 Yt−1 - 0.7776 Yt−2 + 0.1023 - 0.8315 et−1
Long Beans
Yt=0.5460 + 0.3667 Yt−1 + 0.0780 + 0.4322 et−1 + 0.2638 et−2
Bawal, Cencaru, Kembong,
Round Cabbage and Sawi
Jepun
Yt = μ + Yt-1
whereμ : mean of the changes of period to period
Table 4 shows the performance measures of RMSE, MAE and MAPE of ten selected fish and vegetables. The ARIMA
model of Selar Kuning has the least RMSE (0.04) compared to others. Meanwhile, the model of Selar Kuning (0.02) and
Sawi Jepun (0.02) has the minimum MAE. The MAPE of Kembong and Red Bream has the lowest error (0.81) respectively.
Table 4: RMSE, MAE and MAPE of ARIMA Fit Model
Item
RMSE
MAE
MAPE
Bawal
0.32
0.09
1.12
Cencaru
0.14
0.04
1.39
Kembong
0.11
0.03
0.81
Red Bream
0.06
0.03
0.81
Selar Kuning
0.04
0.02
1.57
Green Spinach
0.10
0.05
4.67
Kangkung
0.10
0.05
5.07
Long Beans
0.08
0.05
10.57
Round Cabbage
0.18
0.05
3.43
Sawi Jepun
0.06
0.02
3.84
International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-7 Issue-5S, January 2019
255v
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication
Retrieval Number: ES2152017519/19©BEIESP
Table 5: Forecasted Price of Fish (Malaysia Ringgit)
Date
Bawal
Cencaru
Kembong
Red
Bream
Selar
Kuning
Green
Spinach
Kangkung
Long
Beans
Round
Cabbage
Sawi
Jepun
1/12/2017
0.6
2.84
4.07
2.49
1.51
1.33
1.25
0.61
1.94
0.6
2/12/2017
0.6
2.84
4.07
2.46
1.5
1.28
1.2
0.58
1.94
0.6
3/12/2017
0.6
2.84
4.07
2.46
1.49
1.24
1.16
0.56
1.94
0.6
4/12/2017
0.6
2.84
4.07
2.45
1.49
1.2
1.12
0.55
1.94
0.6
5/12/2017
0.6
2.84
4.07
2.45
1.48
1.17
1.09
0.55
1.94
0.6
6/12/2017
0.6
2.84
4.07
2.44
1.48
1.15
1.07
0.55
1.94
0.6
7/12/2017
0.6
2.84
4.07
2.44
1.47
1.14
1.06
0.55
1.94
0.6
8/12/2017
0.6
2.84
4.07
2.43
1.47
1.13
1.05
0.55
1.94
0.6
9/12/2017
0.6
2.84
4.07
2.43
1.47
1.12
1.04
0.55
1.94
0.6
10/12/2017
0.6
2.84
4.07
2.43
1.47
1.12
1.04
0.55
1.94
0.6
11/12/2017
0.6
2.84
4.07
2.43
1.47
1.12
1.04
0.55
1.94
0.6
12/12/2017
0.6
2.84
4.07
2.42
1.47
1.13
1.05
0.55
1.94
0.6
13/12/2017
0.6
2.84
4.07
2.42
1.47
1.14
1.05
0.55
1.94
0.6
14/12/2017
0.6
2.84
4.07
2.42
1.48
1.14
1.06
0.55
1.94
0.6
The forecasted value of the ten selected fish and
vegetables from 1 December 2017 to 14 December 2017 is
given in Table 5. As seen from the table, Red Bream and
Selar Kuning show variability of the prices with the average
price per pieces. Meanwhile Bawal (RM0.60/pieces),
Cencaru (RM 2.84/pieces) and Kembong (RM4.07/pieces)
show the constant price for next 14 days. Vegetables such as
Green Spinach, Kangkung and Long Beans showed varies
price between 14 days. In the other hand, Round Cabbage
(RM1.94/pieces) and Sawi Jepun (RM0.60/100g) showed
the constant price for next 14 days.
IV. DISCUSSION
The ARIMA technique which applied for forecasting ten
selected fish and vegetables is success by three different
model; ARIMA (0, 1, 0) for Bawal, Cencaru, Kembong,
Round Cabbage and Sawi Jepun; ARIMA (2, 0, 1) with non
zero mean for Red Bream, Selar, Green Spinach and
Kangkung; and ARIMA (1,0, 2) with non zero mean for
Long Beans. The estimated ARIMA model is determined by
the auto.arima function in R.
When comparing to other results, with other researcher,
Abdullah (2012) has the best ARIMA model (2,1,2) of
forecasting the bitcoin gold price. Meanwhile, Paul, Hoque
and Rahman, (2013) also have the same ARIMA (2,1,2)
model. The similarity between this two researches is both
used the daily share price and bitcoin gold price which has
more price movements as compared to the fish and
vegetables price. The period of the time series also
influenced the results of the estimation model. Since this
study only covers 152 days (5 months). Long time series
gives a better forecasting because ARIMA take account the
number of periods in estimating the model.
Based on price behaviour of all ten selected items of fish
and vegetables, the price quite stagnant for a long time
(more than 14 days) and dropped if there are promotions.
Overall, the price range of the selected items is within the
minimum and maximum price range. Tesco is a big
supermarkets, which can buy items in bulk and restock the
items when the items is finished, it also helps the retailer to
control the price which it still in the profit margin even
though the discount is given.
CONCLUSION
As a conclusion, the web scraped data can used to forecast
fish and vegetables using the ARIMA approach. The
estimated ARIMA model can forecast for the next 14 days,
as example. The forecast value of fish and vegetables can be
used in data collection of CPI, in order to monitor the price
behaviour. The government will have the signal when there
is an increase of the price. Based on the forecast,
appropriate solutions can be draw in order to control the
inflation in the country.
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AUTHORS PROFILE
Mazliana Mustapa is working in School of Computing, Asia
Pacific University of Technology and Innovation Malaysia .
Raja Rajeswari Ponnusamy is working in School of
Mathematics, Actuaries and Quantitative Studies, Asia Pacific University
of Technology and Innovation, Malaysia.
Ho Ming Kang is working in School of Mathematics, Actuaries
and Quantitative Studies, Asia Pacific University of Technology and
Innovation , Malaysia .
