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

Autocorrelation

The next characteristic we will analyze is autocorrelation.

Autocorrelation measures how much future values in a time series depend linearly on past values. What examples can we give?

The graph above shows the popularity of the names "Maria" and "Olivia" over 140 years. Olivia's autocorrelation decays much faster than Maria's: this can be explained by the fact that the popularity of the name Olivia was very low until 1980 and then increased very sharply. While the popularity of the name Maria did not have such sharp jumps and developed approximately the same over time.

Let's visualize the autocorrelation:

Let's see how to interpret this chart. The graph shows the last 22 values from the dataset (they are shown as vertical lines). If these lines fall within the shaded blue area, this means that they do not have a significant correlation with the previous values.

As you can see on the graph, the first 13 values are correlated with the previous ones, while the next ones are not.

In summary, autocorrelation is useful for identifying statistically significant relationships between values in a time series.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

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

Everything was clear?

Section 2. Chapter 3

Autocorrelation

The next characteristic we will analyze is autocorrelation.

Autocorrelation measures how much future values in a time series depend linearly on past values. What examples can we give?

Let's visualize the autocorrelation:

As you can see on the graph, the first 13 values are correlated with the previous ones, while the next ones are not.

In summary, autocorrelation is useful for identifying statistically significant relationships between values in a time series.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

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

Everything was clear?

Section 2. Chapter 3

Autocorrelation

The next characteristic we will analyze is autocorrelation.

Autocorrelation measures how much future values in a time series depend linearly on past values. What examples can we give?

Let's visualize the autocorrelation:

As you can see on the graph, the first 13 values are correlated with the previous ones, while the next ones are not.

In summary, autocorrelation is useful for identifying statistically significant relationships between values in a time series.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

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

Everything was clear?

The next characteristic we will analyze is autocorrelation.

Autocorrelation measures how much future values in a time series depend linearly on past values. What examples can we give?

Let's visualize the autocorrelation:

As you can see on the graph, the first 13 values are correlated with the previous ones, while the next ones are not.

In summary, autocorrelation is useful for identifying statistically significant relationships between values in a time series.

Task

Visualize the autocorrelation of the following dataset air_quality_no2_long.csv for 30 records.

Import the plot_acf function from statsmodels.graphics.tsaplots.
Visualize the autocorrelation for 30 "value" records of the data DataFrame.

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

Section 2. Chapter 3

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