Course Content

Time Series Analysis

## Time Series Analysis

1. Time Series: Let's Start

2. Time Series Processing

3. Time Series Visualization

# Simple Time Series

The role of visualization in time series analysis has already been demonstrated to you in the previous section. An expert's glance at the chart may be enough to understand whether the data is stationary or not, what main patterns are present in it, etc.

Despite this, it is important to remember that an expert's assessment is not accurate, and we still recommend using formal methods (tests) to analyze your data.

You already know how to implement 2D visualization:

But let's take as an example a situation where we need to take into account 2 or 3 variables that change over time. For example, you have an online store and a pricing task for which you use competitor pricing data. You take on your 2 main competitors. See how you can put several time series on one graph at once:

What the data looks like in the dataset:

Also, you can implement it in the following way through a loop:

If you have a large amount of data (for example, you want to explore more than 20 competitors), you may have problems with the analysis of the resulting graph:

Then, you can use the `seaborn`

library, which allows you to compare multiple graphs with each other:

Task

Plot the `flights.csv`

dataset, which contains flight data from 1949 to 1960, every month.

- Save months names within the
`all_months`

variable. Months names are`df`

column names. - Iterate over each available year in the data. Years are indexes of the
`df`

dataframe. - Within the loop, filter to iterated year data (using the
`.iloc[]`

property and the`n`

variable), and save them within the`year_row`

variable. - Within the loop, initialize a line plot with months labels (
`all_months`

) on the x-axis, respective data (`year_row`

) on the y-axis, and set`label`

parameter to iterated`year`

. - Display the legend of the plot.

Everything was clear?

# Simple Time Series

The role of visualization in time series analysis has already been demonstrated to you in the previous section. An expert's glance at the chart may be enough to understand whether the data is stationary or not, what main patterns are present in it, etc.

Despite this, it is important to remember that an expert's assessment is not accurate, and we still recommend using formal methods (tests) to analyze your data.

You already know how to implement 2D visualization:

But let's take as an example a situation where we need to take into account 2 or 3 variables that change over time. For example, you have an online store and a pricing task for which you use competitor pricing data. You take on your 2 main competitors. See how you can put several time series on one graph at once:

What the data looks like in the dataset:

Also, you can implement it in the following way through a loop:

If you have a large amount of data (for example, you want to explore more than 20 competitors), you may have problems with the analysis of the resulting graph:

Then, you can use the `seaborn`

library, which allows you to compare multiple graphs with each other:

Task

Plot the `flights.csv`

dataset, which contains flight data from 1949 to 1960, every month.

- Save months names within the
`all_months`

variable. Months names are`df`

column names. - Iterate over each available year in the data. Years are indexes of the
`df`

dataframe. - Within the loop, filter to iterated year data (using the
`.iloc[]`

property and the`n`

variable), and save them within the`year_row`

variable. - Within the loop, initialize a line plot with months labels (
`all_months`

) on the x-axis, respective data (`year_row`

) on the y-axis, and set`label`

parameter to iterated`year`

. - Display the legend of the plot.

Everything was clear?