scholarly journals Bitcoin Analysis and Forecasting Through Fuzzy Transform

2020 ◽  
Author(s):  
Maria Letizia Guerra ◽  
Laerte Sorini ◽  
Luciano Stefanini
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Google Trends ◽  
Fuzzy Transform ◽  
Stylized Facts

Sentiment analysis to characterize properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy transform to capture stylized facts and mutual connections between time series having different nature. Six years of daily Bitcoin Prices and Google Trends are analyzed in order to establish new perspectives in the management of their dynamics.

scholarly journals Bitcoin Analysis and Forecasting through Fuzzy Transform

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 139
Author(s):  
Maria Letizia Guerra ◽  
Laerte Sorini ◽  
Luciano Stefanini
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Test Statistics ◽  
Local Smoothing ◽  
Fuzzy Transform ◽  
Short Term ◽  
Stylized Facts ◽  
Lp Norm ◽  
Daily Data ◽  
Short Term Forecasting

Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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 Time Series Seasonal Analysis Based on Fuzzy Transforms

2017 ◽  
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Time Series ◽  
Seasonal Forecasting ◽  
Weather Station ◽  
Fuzzy Transform ◽  
Italian Region ◽  
Mean Temperature ◽  
Forecasting Method ◽  
Seasonal Analysis ◽  
The Universe ◽  
Universe Of Discourse

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of a an assigned output. Like first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 till to 2015 making predictions on the following outputs: mean temperature, max temperature and min temperature, all considered daily. Like second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, ARIMA and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples.

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 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.

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