scholarly journals Pengaruh Tingkat Minat Penelusuran Antar Penyedia Transportasi Online di Google Trends

2021 ◽  
Vol 2021 (1) ◽  
pp. 968-979
Author(s):  
Vania Orva Nur L ◽  
Paramita Dewanti
Keyword(s):  
Time Series ◽  
Google Trends ◽  
Transfer Model ◽  
Model Time Series

Transportasi online adalah transportasi umum yang penggunaannya menggunakan aplikasi berbasis android dengan tujuan yang lebih efisien dalam hal waktu tempuh dan tarif yang jauh lebih murah. Transportasi online mempunyai banyak kelebihan dibandingkan dengan transportasi konvensional. Tarif yang jauh lebih murah menjadi alasan utama jenis transportasi ini banyak diminati. Hingga saat ini, transportasi online mengalami kemajuan yang sangat pesat di Indonesia. Hampir di semua tempat, transportasi online tersebut dapat dengan mudah ditemui dan juga siap digunakan dengan cara menggunakan aplikasi berbasis android agar bisa menikmati pelayanan transportasi tersebut. Sejak diakuisisnya Uber oleh Grab sejak awal tahun 2018, hanya ada dua penyedia jasa transportasi online yang merajai pasar di Indonesia yaitu Gojek dan Grab. Keduanya saling bersaing untuk menjaring banyak konsumen dengan mengeluarkan banyak promo kepada para penggunanya. Guna melihat pengaruh tingkat minat penelusuran “Grab” terhadap pengaruh tingkat minat penelusuran “Gojek” di Google Trends maka dipergunakanlah model Fungsi Transfer. Model fungsi transfer merupakan salah satu model time series yaitu gabungan pendekatan regresi dan ARIMA pada Analisis Deret Waktu (ADW). Berdasarkan model fungsi transfer dapat diketahui jika tingkat minat penelusuran “Grab” dan “Gojek” di Google Trends tidak saling mepengaruhi satu dengan lainnya.

scholarly journals Reanalysis of the 1992 South Pole Millimetre-Wavelength Atmospheric Opacity Data

2004 ◽  
Vol 21 (3) ◽  
pp. 264-274 ◽  
Author(s):  
Richard A. Chamberlin
Keyword(s):  
Time Series ◽  
Transfer Model ◽  
South Pole ◽  
Millimetre Wave ◽  
Atmospheric Opacity ◽  
Site Survey ◽  
Direct Measurements ◽  
Gain Error ◽  
Sky Brightness ◽  
Total Opacity

AbstractIn 1992 an NRAO 225-GHz site survey heterodyne radiometer was placed at the Geographical South Pole. The instrument operated over an entire annual cycle and provided direct measurements of the millimetre-wave sky brightness temperature as a function of zenith angle. Interpreted in a single-slab ‘skydip’ radiation transfer model of the atmosphere, these sky brightness measurements provided a time series of the millimetre atmospheric opacity. Statistics derived from this opacity time series were important for making comparisons with other candidate millimetre and sub-millimetre wave astronomy sites. This paper reexamines the 1992 measurements and the original analysis. Details of the skydip fit model, radiometer gain error, instrument stability, and a mid-season replacement to a window in the instrument enclosure combined to cause a modest under-reporting of the atmospheric opacity in previous reports. Unchanged are earlier conclusions that dry air makes a significant contribution to the total opacity at 225 GHz.

PERAMALAN TINGKAT PENGANGGURAN DI INDONESIA MENGGUNAKAN METODE TIME SERIES DENGAN MODEL ARIMA DAN HOLT-WINTERS

2021 ◽  
Vol 26 (1) ◽  
pp. 13-28
Author(s):  
Agus Sulaiman ◽  
Asep Juarna
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Beberapa penyebab terjadinya pengangguran di Indonesia ialah, tingkat urbanisasi, tingkat industrialisasi, proporsi angkatan kerja SLTA dan upah minimum provinsi. Faktor-faktor tersebut turut serta mempengaruhi persentase data terkait tingkat pengangguran menjadi sedikit fluktuatif. Berdasarkan pergerakan persentase data tersebut, diperlukan sebuah prediksi untuk mengetahui persentase tingkat pengangguran di masa depan dengan menggunakan konsep peramalan. Pada penelitian ini, peneliti melakukan analisis peramalan time series menggunakan metode Box-Jenkins dengan model Autoregressive Integrated Moving Average (ARIMA) dan metode Exponential Smoothing dengan model Holt-Winters. Pada penelitian ini, peramalan dilakukan dengan menggunakan dataset tingkat pengangguran dari tahun 2005 hingga 2019 per 6 bulan antara Februari hingga Agustus. Peneliti akan melihat evaluasi Range Mean Square Error (RMSE) dan Mean Square Error (MSE) terkecil dari setiap model time series. Berdasarkan hasil penelitian, ARIMA(0,1,12) menjadi model yang terbaik untuk metode Box-Jenkins sedangkan Holt-Winters dengan alpha(mean) = 0.3 dan beta(trend) = 0.4 menjadi yang terbaik pada metode Exponential Smoothing. Pemilihan model terbaik dilanjutkan dengan perbandingan nilai akurasi RMSE dan MSE. Pada model ARIMA(0,1,12) nilai RMSE = 1.01 dan MSE = 1.0201, sedangkan model Holt-Winters menghasilkan nilai RMSE = 0.45 dan MSE = 0.2025. Berdasarkan data tersebut terpilih model Holt-Winters sebagai model terbaik untuk peramalan data tingkat pengangguran di Indonesia.

scholarly journals Fractional differencing in stock market price and online presence of global tourist corporations

2019 ◽  
Vol 24 (48) ◽  
pp. 194-204 ◽  
Author(s):  
Francisco Flores-Muñoz ◽  
Alberto Javier Báez-García ◽  
Josué Gutiérrez-Barroso
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Market Price ◽  
Google Trends ◽  
Data Sets ◽  
Market Prices ◽  
Content Type ◽  
Search Results ◽  
Online Presence ◽  
Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

scholarly journals Seasonal variation and change trends for quit smoking: evidence from Internet search engine query data

2019 ◽  
Author(s):  
Fang Wang ◽  
Dingtao Hu ◽  
Xiaoqi Lou ◽  
Nana Meng ◽  
Qiaomei Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Seasonal Variation ◽  
New Zealand ◽  
Google Trends ◽  
Internet Search ◽  
Quit Smoking ◽  
Significant Seasonal Variation ◽  
The Usa ◽  
The Uk

Abstract Background: The outcomes of smoking have generated considerable clinical interest in recent years. Although people from different countries are more interested to the topic of quit smoking during the winter, few studies have tested this hypothesis. The current study aimed to quantify public interest in quit smoking via Google.Methods: We use Google Trends to obtain the Internet search query volume for terms relating to quit smoking for major northern and southern hemisphere countries in this research. Normally search volumes for the term “quit smoking + stop smoking + smoking cessation” were retrieved within the USA, the UK, Canada, Ireland, New Zealand and Australia from January 2004 to December 2018. Seasonal effects were investigated using cosinor analysis and seasonal decomposition of time series models.Results: Significant seasonal variation patterns in those search terms were revealed by cosinor analysis and demonstrated by the evidence from Google Trends analysis in the representative countries including the USA (pcos = 2.36×10-7), the UK (pcos < 2.00×10-16), Canada (pcos < 2.00×10-16), Ireland (pcos <2.00×10-16) ,Australia (pcos = 5.13×10-6) and New Zealand (pcos = 4.87×10-7). Time series plots emphasized the consistency of seasonal trends with peaks in winter / late autumn by repeating in nearly all years. The overall trend of search volumes, observed by dynamic series analysis, declined from 2004 to 2018.Conclusions: The preliminary evidence from Google Trends search tool showed a significant seasonal variation and decreasing trend for the RSV of quit smoking. Our novel findings in smoking cessation epidemiology need to be verified with further studies, and the mechanisms underlying these findings must be clarified.

scholarly journals VISUALISASI PREDIKSI KUNJUNGAN WISATAWAN MANCANEGARA MENGGUNAKAN MODEL TIME SERIES

JOUTICA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 356
Author(s):  
Ruli Utami ◽  
Mohammad Whildan Indra Maulana
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Series Modeling ◽  
Model Time Series

Sektor pariwisata merupakan sektor dengan prospek tinggi untuk meningkatkan pendapatan negara melalui penerimaan Devisa. Selain itu adanya wisatawan ini juga sangat berdampak pada ekonomi kecil warga sekitar tempat wisata, sehingga sektor ini harus dikelolah dengan bijaksanan. Daya tarik wisata Indonesia sangat menarik minat dari wisatawan mancanegara untuk berkunjung ke Indoensia, hal ini harus sebanding dengan pelayanan publik yang disediakan untuk wisatawan ini; misalnya sarana dan fasilitas hotel serta layanan lain seperti imigrasi. Hal ini dapat dilakukan jika pihak yang berwenang dapat memprediksi jumlah kunjungan wisatawan masing-masing negara. Dari permasalahan tersebut diatas, maka dibuatlah sebuah aplikasi yang bertujuan untuk menampilakn visualisasi prediksi yang dihitung menggunakan pemodelan menggunakan time series modeling dengan visualisasi hasil prediksi melalui sebuah aplikasi. Dari penelitian yang telah dilakukan dengan metode exponential smoothing dapat disimpulkan bahwa nilai parameter yang paling cocok digunakan adalah nilai α = 0.6 dengan nilai MAPE 6.77%.

scholarly journals Examination of the Role of Business Ethics with Google Trends

2019 ◽  
Vol 3 (3) ◽  
pp. 25-38
Author(s):  
Lívia Benita Kiss
Keyword(s):  
Time Series ◽  
Business Ethics ◽  
Applied Ethics ◽  
Business Environment ◽  
Point Of View ◽  
Google Trends ◽  
Scientific Discussion ◽  
Google Books ◽  
Exponential Trend

Ethics has existed in religion and philosophy for thousands of years and has been applied to business activities in the same way ethical values and norms have been applied to everyday life. This article summarizes the arguments and counterarguments within the scientific discussion on the study of business ethics as the form of applied ethics, which studies morals, ethical principles and problems in the business environment. The main goal of the study is to analyze business ethics from the point of view of integration of general morals and ethical norms to business, a combination of key signs of the right (good) or wrong behavior while doing business, determined on the basis of expected behavior approved by the society. The study of the role of business ethics in the corporate sector of the economy allowed to determine the most general principles of business ethics, namely awareness, caring, compliance, consideration, fairness, honesty, implementation, integrity, integration, loyalty, responsibility, and trustworthiness. The methodological basis of the research is analytical, statistical and comparative methods based on the use of Google Trends. In general, in Google Books, the use of the term business ethics shows an exponential trend. The findings show that the highest search frequency of business ethics is in the “all” category, after that in the “business and industry” category, then in the “science” category, finally in the “law and government” category. On average, the highest interest frequency was in 2004 in all examined categories. The author has proved that a third-degree polynomial downward trend can be fitted to each time series. The analysis of this concept on a geographical basis showed that the interest frequency of the principles of business ethics was most significant in South and Central East Africa, in South and Southeast Asia, over and above in the Caribbean. Keywords: business ethics, principles of business ethics, Google Trends, Google Books Ngram Viewer, time series analysis.

Thoughts on Women Entrepreneurship: An Application of Market Basket Analysis with Google Trends Data

2021 ◽  
Author(s):  
Anıl Boz Semerci ◽  
Ayşe Abbasoğlu Özgören ◽  
Duygu İçen
Keyword(s):  
Time Series ◽  
Social Justice ◽  
Geographic Area ◽  
Google Trends ◽  
Digital Data ◽  
Specific Topic ◽  
Market Basket Analysis ◽  
Women Entrepreneurship ◽  
Market Basket ◽  
Regional Setting

Abstract This paper focuses on the popularity and awareness of keywords in Google Trends data related to entrepreneurship of women in a global and cross-regional setting by using market basket analysis. Google Trends is one of the digital data platforms that provides a time series index of the volume of queries users enter into Google in a given geographic area. It is the most popular tool for gathering any information, and it has been used in several topics. Market basket analysis indicates items that appear/used together and the frequency of these appearances. Such technique is appropriate in finding hidden associations between items, which is also crucial in assessing individuals’ thoughts on a specific topic. This study contributes to the literature in terms of being the first study to use market basket analysis on Google trends data in the context of women’s entrepreneurship finding hidden associations between items, which is crucial in assessing individuals’ thoughts on a specific topic. The results of the analysis are interpreted through the lens of genderresponsive strategies, equality, efficiency and social justice in different country and region contexts.

scholarly journals Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 385 ◽  
Author(s):  
David Cuesta-Frau ◽  
Juan Pablo Murillo-Escobar ◽  
Diana Alexandra Orrego ◽  
Edilson Delgado-Trejos
Keyword(s):  
Time Series ◽  
Classification Performance ◽  
Permutation Entropy ◽  
Series Length ◽  
Stability Point ◽  
Long Time ◽  
Model Time Series ◽  
The Stability ◽  
Forbidden Patterns ◽  
Short Time

Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

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