Data Science and Blockchain DSPYT Blog

How to implement Realized Volatility python
How to implement Realized Volatility python

Volatility estimators are especially valuable in modelling financial returns and capturing time-variability of financial series.

Pavel Fedotovdspyt.com profile picture Pavel Fedotov

Pavel Fedotov

Machine Learning time series python - Data Science with Python
Machine Learning time series python - Data Science with Python

Sklearn python pipeline with multiple regression models using traditional and established libraries like numpy, pandas, scipy and sklearn.

Pavel Fedotovdspyt.com profile picture Pavel Fedotov

Pavel Fedotov

Simple App with Ceramic Data Model and Unstoppable Domains - DSPYT
Simple App with Ceramic Data Model and Unstoppable Domains - DSPYT

Ceramic allows users to have complete ownership over their data by providing decentralized technologies for authentication and data storage.

Pavel Fedotovdspyt.com profile picture Pavel Fedotov

Pavel Fedotov

Aurora — EVM on the NEAR Protocol blockchain - DSPYT
Aurora — EVM on the NEAR Protocol blockchain - DSPYT

Aurora delivers Ethereum-compatible, high-throughput, scalable and future-safe platform, with low transaction costs.

Pavel Fedotovdspyt.com profile picture Pavel Fedotov

Pavel Fedotov

Cross-sectional data – An easy introduction
Cross-sectional data – An easy introduction

In this article we are introducing the concept of cross sectional data. A cross sectional data example consists of a sample of units at a given point in time.

Pavel Fedotovdspyt.com profile picture Pavel Fedotov

Pavel Fedotov

Kaggle time series data – An easy introduction
Kaggle time series data – An easy introduction

A time series is a collection of observations on at least one variable ordered along single dimension, time. time series forecasting is invaluable method.

Pavel Fedotovdspyt.com profile picture Pavel Fedotov

Pavel Fedotov