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Jason Webber liked Bohdan Pavlyshenko's blog post Modern Approaches for Sales Predictive Analytics
Sep 8
Bohdan Pavlyshenko commented on Bohdan Pavlyshenko's blog post Modern Approaches for Sales Predictive Analytics
"Hi Anderson Gimenez, Thank you for your comment. I corrected links."
Mar 24
Bohdan Pavlyshenko posted a blog post

Modern Approaches for Sales Predictive Analytics

Sales prediction is an important part of modern business intelligence.  First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and  Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality,  many exogenous  factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is the…See More
Mar 24
Anderson Gimenez commented on Bohdan Pavlyshenko's blog post Modern Approaches for Sales Predictive Analytics
"Hi Bohdan Pavlyshenko, I hope you are going well. I can´t access the link to get the code, when I click in "here" text does not redirect to the code page. Please, could you share with me the code. I am really interesting to see…"
Mar 21
Victor Hugo Calle Gil liked Bohdan Pavlyshenko's blog post Modern Approaches for Sales Predictive Analytics
Mar 15
Bohdan Pavlyshenko shared their blog post on Facebook
Mar 13
Howard Fulks liked Bohdan Pavlyshenko's blog post Modern Approaches for Sales Predictive Analytics
Mar 11
Rick Randall liked Bohdan Pavlyshenko's blog post Modern Approaches for Sales Predictive Analytics
Mar 10
tarek Jan liked Bohdan Pavlyshenko's blog post Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
Mar 10
Bohdan Pavlyshenko posted a blog post

Modern Approaches for Sales Predictive Analytics

Sales prediction is an important part of modern business intelligence.  First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and  Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality,  many exogenous  factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is the…See More
Mar 9
Bohdan Pavlyshenko's blog post was featured

Modern Approaches for Sales Predictive Analytics

Sales prediction is an important part of modern business intelligence.  First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and  Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality,  many exogenous  factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is the…See More
Mar 9
Duane Baker liked Bohdan Pavlyshenko's blog post Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
Dec 14, 2017
Emanuel Woiski liked Bohdan Pavlyshenko's blog post Bitcoin Price Forecasting Using Model with Experts Opinions
Oct 30, 2017
Bohdan Pavlyshenko liked Luba Belokon's blog post Machine Learning Algorithms: Which One to Choose for Your Problem
Oct 29, 2017
Bohdan Pavlyshenko posted a blog post

Bitcoin Price Forecasting Using Model with Experts Opinions

One of the main goals in the Bitcoin analytics is price forecasting. There are many factors which influence the price dynamics. The most important factors are: the interaction between supply and demand, attractiveness for investors, financial and macroeconomics indicators, technical indicators such as difficulty, how many blocks were created recently, etc. A very important impact on the cryptocurrency price has trends in social networks and search engines. Using these factors, one can create a…See More
Oct 29, 2017
Bohdan Pavlyshenko's blog post was featured

Bitcoin Price Forecasting Using Model with Experts Opinions

One of the main goals in the Bitcoin analytics is price forecasting. There are many factors which influence the price dynamics. The most important factors are: the interaction between supply and demand, attractiveness for investors, financial and macroeconomics indicators, technical indicators such as difficulty, how many blocks were created recently, etc. A very important impact on the cryptocurrency price has trends in social networks and search engines. Using these factors, one can create a…See More
Oct 27, 2017

Profile Information

My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/bpavlyshenko/
Professional Status
Consultant
Your Company:
SoftServe
Your Job Title:
Data Scientist
Interests:
Finding a new position, Networking, New venture

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Bohdan Pavlyshenko's Blog

Modern Approaches for Sales Predictive Analytics

Posted on March 8, 2018 at 9:00am 2 Comments

Sales prediction is an important part of modern business intelligence.  First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and  Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality,  many exogenous  factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is…

Continue

Bitcoin Price Forecasting Using Model with Experts Opinions

Posted on October 26, 2017 at 11:30pm 0 Comments

One of the main goals in the Bitcoin analytics is price forecasting. There are many factors which influence the price dynamics. The most important factors are: the interaction between supply and demand, attractiveness for investors, financial and macroeconomics indicators, technical indicators such as difficulty, how many blocks were created recently, etc. A very important impact on the cryptocurrency price has trends…

Continue

Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

Posted on February 26, 2017 at 5:30am 2 Comments

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.

INTRODUCTION

Time series analysis, especially forecasting, is an important problem of modern…

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