17 lines
837 B
TeX
17 lines
837 B
TeX
\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).
|