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Please enable JavaScript in your browser settings or update your browser.Challenge 2 | Non-Stationary Models
Time Series Analysis
course content

Зміст курсу

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

Challenge 2

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 5. Розділ 4
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Challenge 2

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 5. Розділ 4
toggle bottom row

Challenge 2

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Завдання

You are faced with a task similar to the previous one: convert data to stationary. You will remove seasonality in the Seasonality Time Series.csv dataset using the difference method. Track a certain amount of time for which the time pattern repeats and use the difference method:

  1. Read the dataset.
  2. Analyze how long patterns repeat (visualize "Var" and "Date" columns of the df DataFrame in this order).
  3. Use the resulting number (it is 8) for the difference method. Perform the differentiation for the "Var" column. Save the obtained result within the df_diff variable.
  4. Visualize the transformed data.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 5. Розділ 4
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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