<commit_before>Previously in these blog posts, we've looked at predicting some variable
using a bunch of available statistics. In this post, we'll look at using
time to predict a value. Time series models allow us to make predictions
using a more flexible framework for... time series data (crazy, I know)!
There are many time series models, each with their own pros and cons,
but in this post, I'm going to focus on ARIMA models and their
components.

Time Series Basics
==================

A time series is generally made up of three components: trend,
seasonality, and an error term. Trend is the direction of the series
over time; is it going up or down as we move forward? Seasonality is the<commit_msg>Update 2018-7-9-arima.md<commit_after>---
layout: post
title: "Time Series and ARIMA"
date: 2018-07-09 00:00:00
excerpt_separator: <!--more-->
---

Previously in these blog posts, we've looked at predicting some variable
using a bunch of available statistics. In this post, we'll look at using
time to predict a value. Time series models allow us to make predictions
using a more flexible framework for... time series data (crazy, I know)!
There are many time series models, each with their own pros and cons,
but in this post, I'm going to focus on ARIMA models and their
components.

<!--more-->

Time Series Basics
==================

A time series is generally made up of three components: trend,
seasonality, and an error term. Trend is the direction of the series
over time; is it going up or down as we move forward? Seasonality is the
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