Course Content

Time Series Analysis

## Time Series Analysis

1. Time Series: Let's Start

2. Time Series Processing

3. Time Series Visualization

# Simple Moving Average

Mathematically, the simple moving average model is represented as follows:

In this equation, the prediction SMA consists of variables, where `k`

is the window size (how many past values we will take to calculate the next value), and `p`

is the value taken.

How does this model work? In fact, the simple moving average at each prediction moment calculates the average of several past values - the resulting number is the next prediction. You can imagine a graph on the path of which a window of size `n`

moves (you can take 2 values, you can take 3, etc.). The smaller the window, the smoother the predictions. Let's demonstrate it below:

In the first image, the window size for which the average value is calculated is 8, while on the second graph, `n`

= 3. The smaller the window, the fewer peaks it can capture.

Python allows you to implement this model like this:

`rolling()`

- a function used to calculate the moving average.

The simple moving average is one of the simplest models, so it is enough to use the `pandas`

library to implement it.

Task

Make predictions for dataset `pr_air_quality.csv`

with window size 5.

- Convert the
`"date.utc"`

column to`datetime`

type. - Calculate moving averages using moving windows with the size of
`5`

. - Compare the results on a plot: visualize the first 50 values of the
`"value"`

column of the`df`

within the first call of the`.plot()`

function and the first 50 values of the`pred`

within the second call. - Display the legend and the plot.

Everything was clear?

# Simple Moving Average

Mathematically, the simple moving average model is represented as follows:

In this equation, the prediction SMA consists of variables, where `k`

is the window size (how many past values we will take to calculate the next value), and `p`

is the value taken.

How does this model work? In fact, the simple moving average at each prediction moment calculates the average of several past values - the resulting number is the next prediction. You can imagine a graph on the path of which a window of size `n`

moves (you can take 2 values, you can take 3, etc.). The smaller the window, the smoother the predictions. Let's demonstrate it below:

In the first image, the window size for which the average value is calculated is 8, while on the second graph, `n`

= 3. The smaller the window, the fewer peaks it can capture.

Python allows you to implement this model like this:

`rolling()`

- a function used to calculate the moving average.

The simple moving average is one of the simplest models, so it is enough to use the `pandas`

library to implement it.

Task

Make predictions for dataset `pr_air_quality.csv`

with window size 5.

- Convert the
`"date.utc"`

column to`datetime`

type. - Calculate moving averages using moving windows with the size of
`5`

. - Compare the results on a plot: visualize the first 50 values of the
`"value"`

column of the`df`

within the first call of the`.plot()`

function and the first 50 values of the`pred`

within the second call. - Display the legend and the plot.

Everything was clear?