  This paper focuses on the hypothesis of optimizing time series predictions
using fractal interpolation techniques. In general, the accuracy of machine
learning model predictions is closely related to the quality and quantitative
aspects of the data used, following the principle of \textit{garbage-in,
garbage-out}. In order to quantitatively and qualitatively augment datasets,
one of the most prevalent concerns of data scientists is to generate synthetic
data, which should follow as closely as possible the actual pattern of the
original data.
  This study proposes three different data augmentation strategies based on
fractal interpolation, namely the \textit{Closest Hurst Strategy},
\textit{Closest Values Strategy} and \textit{Formula Strategy}. To validate the
strategies, we used four public datasets from the literature, as well as a
private dataset obtained from meteorological records in the city of Brasov,
Romania. The prediction results obtained with the LSTM model using the
presented interpolation strategies showed a significant accuracy improvement
compared to the raw datasets, thus providing a possible answer to practical
problems in the field of remote sensing and sensor sensitivity. Moreover, our
methodologies answer some optimization-related open questions for the fractal
interpolation step using \textit{Optuna} framework.
