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

Reading 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.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Task

Read and visualize the AirPassengers.csv dataset.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 2. Chapter 1

Reading 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.

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.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Task

Read and visualize the AirPassengers.csv dataset.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 2. Chapter 1

Reading 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.

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.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Task

Read and visualize the AirPassengers.csv dataset.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

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.

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.

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

"Month" on the x-axis;
"Passengers" on the y-axis.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 2. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below