Forecasting Prices of Fish and Vegetable using Web Scraped Price Micro Data
Mazliana Mustapa1, Raja Rajeswari Ponnusamy2, Ho Ming Kang3

1Mazliana Mustapa, Working, School of Computing, Asia Pacific University of Technology and Innovation Malaysia.
2Raja Rajeswari Ponnusamy, Working, School of Mathematics, Actuaries and Quantitative Studies, Asia Pacific University of Technology and Innovation, Malaysia.
3Ho Ming Kang, Working, School of Mathematics, Actuaries and Quantitative Studies, Asia Pacific University of Technology and Innovation, Malaysia.
Manuscript received on 05 February 2019 | Revised Manuscript received on 11 February 2019 | Manuscript Published on 19 February 2019 | PP: 251-256 | Volume-7 Issue-5S January 2019 | Retrieval Number: ES2152017519/19©BEIESP
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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.
Scope of the Article: Web Technologies