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Time Series Analysis
course content

Course Content

Time Series Analysis

Time Series Analysis

1. Time Series: Let's Start
IntroductionPractical ExamplesModel TypesImplementation in PythonVisualization
2. Time Series Processing
Reading and Visualizing DataStationarityAutocorrelationDecomposition
3. Time Series Visualization
Simple Time SeriesCustomize Your PlotChallenge
4. Stationary Models
Examples of Stationary Time SeriesSimple Moving AverageAutoregressionAutoregressive-Integrated-Moving-Average ModelChallenge
5. Non-Stationary Models
Examples of Non-Stationary Time SeriesConvert Non-Stationary Data to StationaryChallenge 1Challenge 2
6. Solve Real Problems
Weather ForecastingStore Demand ForecastFinancial MarketsPredict the Emergence of Repeat CustomersMedicine: EEG Forecasting

bookCustomize 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
test

Swipe to show code editor

Visualize the dataset pr_air_quality.csv.

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 2
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bookCustomize 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
test

Swipe to show code editor

Visualize the dataset pr_air_quality.csv.

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 2
toggle bottom row

bookCustomize 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
test

Swipe to show code editor

Visualize the dataset pr_air_quality.csv.

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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
test

Swipe to show code editor

Visualize the dataset pr_air_quality.csv.

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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