Data for: Forecasting in Non-stationary Environments with Fuzzy Time Series

Published: 23 October 2020| Version 1 | DOI: 10.17632/np9hmp92dj.1
, Frederico Guimaraes


The datasets consist of four stock market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), two cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD) and eight synthetic time series with concept drifts. The market indexes data sets contain the daily averaged index by business day, such that the Dow Jones Industrial Average (Dow Jones) is sampled from 1985 to 2017 time window, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) is sampled from 1995 to 2014, the National Association of Securities Dealers Automated Quotations - Composite Index (NASDAQ ÎXIC) is sampled from 2000 to 2016 and the SP500 - Standard & Poor's 500 is sampled from 1950 to 2017. The FOREX data sets contain the daily averaged quotations, by business day, from 2016 to 2018, which pairs are the US Dollar to Euro (USD-EUR), Euro to Great British Pound (EUR-GBP) and Great British Pound to US Dollar (GBP-USD). The cryptocoin datasets contain the daily quotations, in US Dollar, of the Bitcoin (BTC-USD) and Ethereum (ETC-USD). The synthetic data aims to represent different types of concept drifts.



Financial Time Series Analysis, Online Learning, Time Series Forecasting, Concept Drift