Course Content

Time Series Analysis

1. Time Series: Let's Start

Introduction Practical Examples Model Types Implementation in Python Visualization

2. Time Series Processing

Reading and Visualizing Data Stationarity Autocorrelation Decomposition

3. Time Series Visualization

Simple Time Series Customize Your Plot Challenge

4. Stationary Models

Examples of Stationary Time Series Simple Moving Average Autoregression Autoregressive-Integrated-Moving-Average Model Challenge

5. Non-Stationary Models

Examples of Non-Stationary Time Series Convert Non-Stationary Data to Stationary Challenge 1 Challenge 2

6. Solve Real Problems

Weather Forecasting Store Demand Forecast Financial Markets Predict the Emergence of Repeat Customers Medicine: EEG Forecasting

Time Series Analysis

Customize Your Plot

Now we will look at how you can diversify and improve the presentation of your graphs. This will improve the perception of the graph and can also be useful when creating presentations and other documents with data analysis.

Let's start by adding headings to the graph:

You can also change the style of the time series chart. If you want the graph to be not a solid line but consist of dots, then change the value of the linestyle argument to "dotted":

And finally, let's figure out how to change the chart's color scheme. You have a huge selection of colormaps, a complete list of which is on the official matplotlib website.

To change the color palette, you just need to change the value of the colormap argument to the name of the palette you are interested in:

Task

Visualize the dataset pr_air_quality.csv.

Read the csv file.
Initialize a line plot for the "value" column of df. Set "dotted" linestyle and "cool" colormap.
Add plot title "Air quality analysis".
Add labels on the axis: "Datetime" on the x-axis and "Value" on the y-axis.

Everything was clear?

Section 3. Chapter 2

Course Content

Time Series Analysis

1. Time Series: Let's Start

Introduction Practical Examples Model Types Implementation in Python Visualization

2. Time Series Processing

Reading and Visualizing Data Stationarity Autocorrelation Decomposition

3. Time Series Visualization

Simple Time Series Customize Your Plot Challenge

4. Stationary Models

Examples of Stationary Time Series Simple Moving Average Autoregression Autoregressive-Integrated-Moving-Average Model Challenge

5. Non-Stationary Models

Examples of Non-Stationary Time Series Convert Non-Stationary Data to Stationary Challenge 1 Challenge 2

6. Solve Real Problems

Weather Forecasting Store Demand Forecast Financial Markets Predict the Emergence of Repeat Customers Medicine: EEG Forecasting

Time Series Analysis

Customize Your Plot

Let's start by adding headings to the graph:

You can also change the style of the time series chart. If you want the graph to be not a solid line but consist of dots, then change the value of the linestyle argument to "dotted":

And finally, let's figure out how to change the chart's color scheme. You have a huge selection of colormaps, a complete list of which is on the official matplotlib website.

To change the color palette, you just need to change the value of the colormap argument to the name of the palette you are interested in:

Task

Visualize the dataset pr_air_quality.csv.

Read the csv file.
Initialize a line plot for the "value" column of df. Set "dotted" linestyle and "cool" colormap.
Add plot title "Air quality analysis".
Add labels on the axis: "Datetime" on the x-axis and "Value" on the y-axis.

Everything was clear?

Section 3. Chapter 2