  In Internet of things (IoT), data is continuously recorded from different
data sources and devices can suffer faults in their embedded electronics, thus
leading to a high-dimensional data sets and concept drift events. Therefore,
methods that are capable of high-dimensional non-stationary time series are of
great value in IoT applications. Fuzzy Time Series (FTS) models stand out as
data-driven non-parametric models of easy implementation and high accuracy.
Unfortunately, FTS encounters difficulties when dealing with data sets of many
variables and scenarios with concept drift. We present a new approach to handle
high-dimensional non-stationary time series, by projecting the original
high-dimensional data into a low dimensional embedding space and using FTS
approach. Combining these techniques enables a better representation of the
complex content of non-stationary multivariate time series and accurate
forecasts. Our model is able to explain 98% of the variance and reach 11.52% of
RMSE, 2.68% of MAE and 2.91% of MAPE.
