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This repository is part of an article about how to forecast and detect anomalies on time-series data. The main objective is to train a RNN regressor on the Bitcoin dataset to predict future values on then detect anomalies in the whole data window - that last step achieved by implementing a RNN Autoencoder. You'll see some other models in the notebooks that I've provided to you in case they are of your interest and this RNN regressor + RNN Autoencoder doesn't perform well for your purpose in any other scenario. The dataset used is available at https://www.kaggle.com/mczielinski/bitcoin-historical-data and contains BITCOIN/USD 1-minute candle data, from 2012-01-01 to 2020-12-31. I hope you can get advantage of this approach!

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anomalydetection's Introduction

Саламатсызбы!

Я представляю собственную вэб-студию Everybit в Бишкеке, Кыргызстане и предлагаю следующие услуги:

  1. Технические. Автоматизация (программирование и проч.) :
  • создание корпоративных порталов, интернет-магазинов
  • техническое обслуживание интернет-ресурсов

  1. Управление качеством в "Цикле цепей поставок" - от закупки сырья до постпродажного обслуживания

  2. Маркетинговые. Вводный маркетинговый консалтинг для собственников предприятий ("владельцев продукта"), желающих обогатить свой бизнес потенциалом интернета и информационных технологий в целом. Как результат, у Вас будет понимание, что ожидать от таких явлений СМИ, ИТ и SEO и соответствующих специалистов.

Greetings!

I represent a web studio in Bishkek, Kyrgyzstan and Kazakhstan, and globally too. I offer the following services.

  1. IT, programming:
  • developmlent of corporate portals, e-commerce web-sites
  • technical administration for web portals
  1. Marketing. Initial internet-marketing consulting for business and product owners.

С уважением/Regards, Тимур Х. /Timur K.

Руководитель, ведущий разработчик

Director, lead developer

+996 505 369950

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PS: Goodies page

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