# Moving Average/Basic Approach

## Generic Approach to Moving Average

An element $v\in V$ moves in an additive Group (mathematics) or Vector Space V. In a generic approach, we have a moving probability distribution $P_{v}$ that defines how the values in the environment of $v\in V$ have an impact on the moving average.

### Discrete/continuous Moving Average

According to probability distributions we have to distinguish between a

• discrete (probability mass function $p_{v}$ ) and
• continuous (probability density function $p_{v}$ )

moving average. The terminology refers to probability distributions and the semantics of probability mass/density function describes the distrubtion of weights around the value $v\in V$ . In the discrete setting the $p_{v}(x)=0.2$ means that $x$ has a 20% impact on the moving average $MA(v)$ for $v$ .

### Moving/Shift Distributions

If the probility distribution are shifted by $v$ in $V$ . This means that the probability mass functions $p_{v}$ resp. probability density functions $p_{v}$ are generated by a probability distribution $p_{0}$ at the zero element of the additive group resp. zero vector of the vector space. Due to nature of the collected data f(x) exists for a subset $T\subseteq V$ . In many cases T are the points in time for which data is collected. The and the shift of a distribution is defined by the following property:

• discrete: For all $x\in V$ the probability mass function fulfills $p_{v}(x):=p_{0}(x-v)$ for $v\in T$ • continuous: For all probability density function fulfills $p_{v}(x):=p_{0}(x-v)$ The moving average is defined by:

• discrete: (probability mass function $p_{v}$ )
$MA(v):=\sum _{x\in T}p_{v}(x)\cdot f(x)$ Remark: $p_{v}(x)>0$ for a countable subset of $V$ • continuous probability density function $p_{v}$ $MA(v):=\int _{T}p_{v}(x)\cdot f(x)\,dx$ It is important for the definition of probability mass functions resp. probability density functions $p_{v}$ that the support (measure theory) of $p_{v}$ is a subset of T. This assures that 100% of the probability mass is assigned to collected data. The support $p_{v}$ is defined as:

$\mathrm {supp} (p_{v}):={\overline {\{x\in V\mid p_{v}(x)>0\}}}\subset T.$ 