  As more connected devices are implemented in a cyber-physical world and data
is expected to be collected and processed in real time, the ability to handle
time series data has become increasingly significant. To help analyze time
series in data mining applications, many time series representation approaches
have been proposed to convert a raw time series into another series for
representing the original time series. However, existing approaches are not
designed for open-ended time series (which is a sequence of data points being
continuously collected at a fixed interval without any length limit) because
these approaches need to know the total length of the target time series in
advance and pre-process the entire time series using normalization methods.
Furthermore, many representation approaches require users to configure and tune
some parameters beforehand in order to achieve satisfactory representation
results. In this paper, we propose NP-Free, a real-time Normalization-free and
Parameter-tuning-free representation approach for open-ended time series.
Without needing to use any normalization method or tune any parameter, NP-Free
can generate a representation for a raw time series on the fly by converting
each data point of the time series into a root-mean-square error (RMSE) value
based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward
strategy. To demonstrate the capability of NP-Free in representing time series,
we conducted several experiments based on real-world open-source time series
datasets. We also evaluated the time consumption of NP-Free in generating
representations.
