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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

bookReading and Visualizing Data

The first thing to start with is reading the data. When working with time series, the rules of the game do not change - you can still use pandas to get data from csv files.

In the files, let's say you have a Date column that contains dates in str type. For further time series analysis, you must turn the str type into a datetime. This is implemented using the pandas function to_datetime()

Let's take the dataset air_quality_no2_long.csv as an example:

Next, we convert the data type in the Date column from str to datetime:

You can also do this immediately when reading the dataset:

Now we can plot our dataset:

Task

Read and visualize the AirPassengers.csv dataset.

  1. Import matplotlib.pyplot as plt.
  2. Read the csv file and save it within the data variable.
  3. Convert "Month" into datetime type.
  4. Initialize a line plot with the "Month" column of data on the x-axis and "#Passengers" on the y-axis.
  5. Set labels on an axis and display the plot:
  • "Month" on the x-axis;
  • "Passengers" on the y-axis.

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Section 2. Chapter 1
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bookReading and Visualizing Data

The first thing to start with is reading the data. When working with time series, the rules of the game do not change - you can still use pandas to get data from csv files.

In the files, let's say you have a Date column that contains dates in str type. For further time series analysis, you must turn the str type into a datetime. This is implemented using the pandas function to_datetime()

Let's take the dataset air_quality_no2_long.csv as an example:

Next, we convert the data type in the Date column from str to datetime:

You can also do this immediately when reading the dataset:

Now we can plot our dataset:

Task

Read and visualize the AirPassengers.csv dataset.

  1. Import matplotlib.pyplot as plt.
  2. Read the csv file and save it within the data variable.
  3. Convert "Month" into datetime type.
  4. Initialize a line plot with the "Month" column of data on the x-axis and "#Passengers" on the y-axis.
  5. Set labels on an axis and display the plot:
  • "Month" on the x-axis;
  • "Passengers" 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 2. Chapter 1
toggle bottom row

bookReading and Visualizing Data

The first thing to start with is reading the data. When working with time series, the rules of the game do not change - you can still use pandas to get data from csv files.

In the files, let's say you have a Date column that contains dates in str type. For further time series analysis, you must turn the str type into a datetime. This is implemented using the pandas function to_datetime()

Let's take the dataset air_quality_no2_long.csv as an example:

Next, we convert the data type in the Date column from str to datetime:

You can also do this immediately when reading the dataset:

Now we can plot our dataset:

Task

Read and visualize the AirPassengers.csv dataset.

  1. Import matplotlib.pyplot as plt.
  2. Read the csv file and save it within the data variable.
  3. Convert "Month" into datetime type.
  4. Initialize a line plot with the "Month" column of data on the x-axis and "#Passengers" on the y-axis.
  5. Set labels on an axis and display the plot:
  • "Month" on the x-axis;
  • "Passengers" 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!

The first thing to start with is reading the data. When working with time series, the rules of the game do not change - you can still use pandas to get data from csv files.

In the files, let's say you have a Date column that contains dates in str type. For further time series analysis, you must turn the str type into a datetime. This is implemented using the pandas function to_datetime()

Let's take the dataset air_quality_no2_long.csv as an example:

Next, we convert the data type in the Date column from str to datetime:

You can also do this immediately when reading the dataset:

Now we can plot our dataset:

Task

Read and visualize the AirPassengers.csv dataset.

  1. Import matplotlib.pyplot as plt.
  2. Read the csv file and save it within the data variable.
  3. Convert "Month" into datetime type.
  4. Initialize a line plot with the "Month" column of data on the x-axis and "#Passengers" on the y-axis.
  5. Set labels on an axis and display the plot:
  • "Month" on the x-axis;
  • "Passengers" on the y-axis.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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