Bohdan Pavlyshenko
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Data Scientist
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Finding a new position, Networking, New venture

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Bohdan Pavlyshenko's Blog

Bayesian Model for COVID-19 Spread Prediction

Posted on April 3, 2020 at 12:10am 0 Comments

Bayesian Model for COVID-19 Spread Prediction

At present time, there are different methods, approaches, data sets for for modeling COVID-19 spread [1, 2, 3, 4, 5, 6]. For the predictive analytics of COVID-19 spread, we used a logistic curve model. Such model is very popular nowadays. To estimate model parameters, we used Bayesian regression [7, 8, 9]. This approach allows us to receive a posterior distribution of model parameters…


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…


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…


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.


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


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