# Predictions Using Time Series Data

## #Seasonality

## General Phenological Model for Seasonality

In business, time series data $f(t)$ usually carries information about trend $g(t)$ ($g$ is used since trend is usually growth), seasonalities (periodical effects) $p(t)$, holiday effects (structural effects) $s(t)$, etc. We will decompose a time series $f(t)$ into four components

$$ \begin{equation} f(t) = g(t) + p(t) + s(t) + \epsilon(t). \end{equation} $$

To train a model for the predictions, we need to write down the exact models of these three predictable components.

Published:
by Lei Ma;

Lei Ma (2019). 'Predictions Using Time Series Data', Datumorphism, 06 April. Available at: https://datumorphism.leima.is/wiki/time-series/predictions-time-series-data/.

## Table of Contents

**Current Ref:**

- wiki/time-series/predictions-time-series-data.md