<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">\section{Literature Review}
\label{lit}

In this section, we review the specific forecasting methods that make up our
    forecasting system.
We group them into classical statistics and ML models.
The two groups differ mainly in how they represent the input data and how
    accuracy is evaluated.

A time series is a finite and ordered sequence of equally spaced observations.
Thus, time is regarded as discrete and a time step as a short period.
Formally, a time series $Y$ is defined as $Y = \{y_t: t \in I\}$, or $y_t$ for
    short, where $I$ is an index set of positive integers.
Besides its length $T = |Y|$, another property is the a priori fixed and
    non-negative periodicity $k$ of a seasonal pattern in demand:
$k$ is the number of time steps after which a pattern repeats itself (e.g.,
    $k=12$ for monthly sales data).
</pre></body></html>