scholarly journals Commodity Price Data Analysis Using Web Scraping

2015 ◽  
Vol 4 (4) ◽  
pp. 146
Author(s):  
M. Kameswara Rao ◽  
Rohit Lagisetty ◽  
M.S.V.K. Maniraj ◽  
K.N.S. Dattu ◽  
B. Sneha Ganga
Keyword(s):  
Data Analysis ◽  
Price Index ◽  
Inflation Rate ◽  
Commodity Price ◽  
Entire Process ◽  
Price Data ◽  
Web Scraping ◽  
Visualization Of Data ◽  
Price Analysis ◽  
The Web

<p>Today, analysis of data which is available on the web has become more popular, by using such data we are capable to solve many issues. Our project deals with the analysis of commodity price data available on the web. In general, commodity price data analysis is performed to know inflation rate prevailing in the country and also to know cost price index (CPI). Presently in some countries this analysis is done manually by collecting data from different cities, then calculate inflation and CPI using some predefined formulae. To make this entire process automatic we are developing this project. Now a day’s most of the customers are depending on online websites for their day to day purchases. This is the reason we are implementing a system to collect the data available in various e-commerce sites for commodity price analysis. Here, we are going to introduce a data scraping technique which enables us to collect data of various products available online and then store it in a database there after we perform analysis on them. By this process we can reduce the burden of collecting data manually by reaching various cities. The system consists of web module which perform analysis and visualization of data available in the database.</p>

Web scraping for food price research

2019 ◽  
Vol 121 (12) ◽  
pp. 3350-3361 ◽  
Author(s):  
Judith Hillen
Keyword(s):  
Data Collection ◽  
Food Price ◽  
Content Type ◽  
Price Data ◽  
Technical Procedure ◽  
Web Scraping ◽  
Consumer Prices ◽  
Legal Challenges ◽  
Data Collection Technique ◽  
Price Analysis

Purpose The purpose of this paper is to discuss web scraping as a method for extracting large amounts of data from online sources. The author wants to raise awareness of the method’s potential in the field of food price research, hoping to enable fellow researchers to apply this method. Design/methodology/approach The author explains the technical procedure of web scraping, reviews the existing literature, and identifies areas of application and limitations for food price research. Findings The author finds that web scraping is a promising method to collect customised, high-frequency data in real time, overcoming several limitations of currently used food price data sources. With today’s applications mostly focussing on (online) consumer prices, the scope of applications for web scraping broadens as more and more price data are published online. Research limitations/implications To better deal with the technical and legal challenges of web scraping and to exploit its scalability, joint data collection projects in the field of agricultural and food economics should be considered. Originality/value In agricultural and food economics, web scraping as a data collection technique has received little attention. This is one of the first articles to address this topic with particular focus on food price analysis.

scholarly journals Cost Snipper – Predicting Prices of Online Shopping Items based on Preceding Data

2020 ◽  
Vol 8 (6) ◽  
pp. 4405-4408
Keyword(s):  
Online Shopping ◽  
Price Prediction ◽  
Price Data ◽  
Web Scraping ◽  
The Future ◽  
The Web

The variation of product prices in online shopping is high which makes it difficult to decide when to buy. The tremendous growth of e-commerce helps us to create the solution of price prediction. We used web scraping technique to get the price data from various online shopping retailers and process the data for each commodity to predict the price for the future which helps us to make decisions on buying online products. We automated the web scraping of data and price prediction daily to make the price available for the customer without any delay.

Extending the Stochastic Approach to Paasches Price Index Numbers

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Dr. S.M. Aqil Burney ◽  
Arfa Maqsood
Keyword(s):  
Standard Error ◽  
Price Index ◽  
Inflation Rate ◽  
Stochastic Approach ◽  
Index Number ◽  
Index Numbers ◽  
Price Data ◽  
Laspeyres Index

<span>In recent times, the stochastic approach has received enormous attention to estimate the rate of<span> inflation. The attraction of this approach is to provide not only the estimate of inflation rate, but<span> also its standard error. In this paper, we extend the stochastic approach to derive the Paasches<span> price index number and its standard error. We present an illustration to Laspeyres index number<span> using consumer price data of Pakistan covering the period from July 2002 to June 2011<br /><br class="Apple-interchange-newline" /></span></span></span></span></span>

Uji Kesukaan Es Krim Kefir Labu Kuning

2017 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Amanda Kania Diandini
Keyword(s):  
Diabetes Mellitus ◽  
Data Analysis ◽  
Experimental Method ◽  
Skim Milk ◽  
Ice Cream ◽  
Artificial Sweetener ◽  
Value Analysis ◽  
Nutrient Value ◽  
Bioactive Content ◽  
Price Analysis

The safe ice cream which is consumed by Diabetes Mellitus sufferers is made by subtituting skim milk, cream and sugar with kefir, pure pumpkin, cornstarch, vegetable oil, and artificial sweetener special gor Diabetes Mellitus. Kefir is known can decrease (blood sugar) because of its bioactive content. The aim of this research is knowing predilection level test to ice cream pumpkin kefir. This research is conducted with experimental method. The data analysis includes predilection test, nutrient value analysis, and price analysis. Ice cream pumpkin kefir that is liked most are from texture side, the cheapest price, and also the highest fiber content exists in balance 578 with the ratio of kefir and pumpkin 50%:50%. Ice cream pumpkin kefir that is liked most from colour side exists in balance 236 with the ratio of kefir and pure pumpkin 70%:30%. Ice cream pumpkin kefir that is liked most from taste side and aroma exists in balance 522 with the ratio between kefir and pure pumpkin 80%:20%.

Can energy commodities affect energy blockchain-based cryptos?

2019 ◽  
Vol 36 (4) ◽  
pp. 682-699 ◽  
Author(s):  
Ikhlaas Gurrib
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Autoregressive Model ◽  
Structural Breaks ◽  
Price Index ◽  
Principal Component ◽  
Commodity Price ◽  
Var Model ◽  
Content Type ◽  
Heating Oil

Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

scholarly journals Exploring the Potentialities of Automatic Extraction of University Webometric Information

2020 ◽  
Vol 5 (4) ◽  
pp. 43-55
Author(s):  
Gianpiero Bianchi ◽  
Renato Bruni ◽  
Cinzia Daraio ◽  
Antonio Laureti Palma ◽  
Giulio Perani ◽  
...  
Keyword(s):  
Text Mining ◽  
Web Mining ◽  
Knowledge Extraction ◽  
Automatic Extraction ◽  
Mining Operations ◽  
Automatic Data ◽  
Link Type ◽  
Web Scraping ◽  
University Systems ◽  
The Web

AbstractPurposeThe main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities.Design/methodology/approachWebometrics relies on web mining methods and techniques to perform quantitative analyses of the web. This study implements an advanced application of the webometric approach, exploiting all the three categories of web mining: web content mining; web structure mining; web usage mining. The information to compute our indicators has been extracted from the universities’ websites by using web scraping and text mining techniques. The scraped information has been stored in a NoSQL DB according to a semi-structured form to allow for retrieving information efficiently by text mining techniques. This provides increased flexibility in the design of new indicators, opening the door to new types of analyses. Some data have also been collected by means of batch interrogations of search engines (Bing, www.bing.com) or from a leading provider of Web analytics (SimilarWeb, http://www.similarweb.com). The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register (https://eter.joanneum.at/#/home), a database collecting information on Higher Education Institutions (HEIs) at European level. All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators.FindingsThe main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’ websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.Research limitationsThe results reported in this study refers to Italian universities only, but the approach could be extended to other university systems abroad.Practical implicationsThe approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites. The approach could be applied to other university systems.Originality/valueThis work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping, optical character recognition and nontrivial text mining operations (Bruni & Bianchi, 2020).

scholarly journals PENGEMBANGAN E-MODUL BERBASIS PROJECT BASED LEARNING PADA MATA PELAJARAN PEMROGRAMAN WEB KELAS X DI SMK NEGERI 3 SINGARAJA

2016 ◽  
Vol 13 (2) ◽  
Author(s):  
I Kadek Adi Winaya ◽  
I Gede Mahendra Darmawiguna ◽  
I Gede Partha Sindu
Keyword(s):  
Data Analysis ◽  
Project Based Learning ◽  
Student Response ◽  
Response Data ◽  
Design And Implementation ◽  
Web Programming ◽  
Engineering Department ◽  
Model Project ◽  
Academic Year ◽  
The Web

AbstrakPenelitian ini bertujuan (1) Untuk merancang dan mengimplementasikan Pengembangan E-modul Berbasis Project Based Learningpada Mata Pelajaran Pemrograman Web Kelas X  di SMK Negeri 3 Singaraja. (2)Untuk mengetahui respon siswa dan guru terhadap Pengembangan E-modul Berbasis Project Based Learningpada Mata Pelajaran Pemrograman Web Kelas X  di SMK Negeri 3 Singaraja.Metode penelitian yang digunakan dalam penelitian ini adalah penelitian dan pengembangan. dengan model pengembangan ADDIE. Subjek penelitian ini yaitu siswa kelas X Teknik Komputer dan Jaringan dan guru mata pelajaran Pemrograman Web di SMK Negeri 3 Singaraja tahun ajaran 2016/2017. Untuk mengetahui respon siswa dan guru terhadap e-modul diperoleh dengan menggunakan metode angket.Hasil penelitian menunjukkan bahwa: 1) Hasil rancangan dan implementasi e-modul yang telah dikembangkan pada mata pelajaran pemrograman web untuk siswa kelas X Teknik Komputer dan Jaringan dengan menggunakan model pembelajaran Project Based Learning di SMK Negeri 3 Singaraja dinyatakan berhasil diterapkan berdasarkan beberapa uji yang dilakukan. 2) Hasil analisis data respon siswa menunjukkan bahwa persentase siswa yang memberikan respon sangat baik sebesar 16%, persentase siswa yang memberikan respon baik sebesar 84%, dan tidak ada siswa yang memberikan respon cukup, kurang, maupun sangat kurang. Sedangkan hasil analisis data respon guru menunjukkan bahwa persentase guru yang memberikan respon sangat baik sebesar 100%, dan tidak ada guru yang memberikan respon baik, cukup, kurang, maupun sangat kurang. Kata kunci:  E-Modul, Pemrograman Web, Model Project Based Learning AbstractThe purposes of this research were (1) To design and implement of the Development of E-module based the Project Based Learning on the Web Programming Subject of the X grade of SMK Negeri 3 Singaraja. (2) To know the students and the teachers response toward the Development of E-module based of Project Based Learning on the Web Programming Subject in X grade of SMK Negeri 3 Singaraja.The method that used in this research was research and development by ADDIE development model. The subjects of the study were all of the students and the teachers of X grade of Computer and Networking Engineering department of SMK Negeri 3 Singaraja in the academic year 2016/2017. To know the students and the teachers response toward the e-module obtained was collected by questionnaires method.The results has showed that: 1) The design and implementation of e-module that was developed on the web programming for the students of X grade of Computerand Networking Engineering department of SMK Negeri 3 Singaraja by using model Project Based Learning was successfully applied according the several test that has done before, 2) The results of the data analysis of students response have indicated that the percentage of 16% were good, the precentage of good were 84%, and no student response was for moderate, deficient, nor very deficient. Besides, the results of the teachers response data analysis showed that the percentage of 100% for very well response and no teacher gave for good, moderate, deficient, nor very deficient. Keywords : E-Module, Web Programming, Project Based Learning Model

scholarly journals The Effect of Macroeconomic Variables On the Saudi Stock Market

2019 ◽  
Vol 7 (12) ◽  
pp. 126-152
Author(s):  
Amani Mohammed Aldukhail
Keyword(s):  
Stock Market ◽  
Performance Indicators ◽  
Stock Price ◽  
Price Index ◽  
Inflation Rate ◽  
Market Value ◽  
Short Term ◽  
Macroeconomic Variables ◽  
Time Deposits

This study aimed at exploring the effect of macroeconomic variables on the activity of the Saudi stock market for the period 1997-2017. Macroeconomic variables were: GDP, interest rate on time deposits, inflation rate. The variables of the Saudi stock market activity were: stock price index, market value of shares, value of traded shares. To achieve this objective, the researcher used the ARDL model for the self-regression of the lagged distributed time gaps. The most important results of the research are: The effect of macroeconomic variables on the performance indicators in the Saudi stock market is not important in the short term and is statistically significant in the long term according to the proposed models, so investors in this market can rely on macroeconomic variables in Predict the movement of the stock market and predict long-term profits and losses.

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