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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

bookAutocorrelation

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
test

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

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

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Section 2. Chapter 3
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bookAutocorrelation

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
test

Swipe to show code editor

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

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

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 3
toggle bottom row

bookAutocorrelation

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
test

Swipe to show code editor

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

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

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 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
test

Swipe to show code editor

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

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

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