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Time Series Analysis
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Kursinnehåll

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

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Challenge 2

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Swipe to start coding

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.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 5. Kapitel 4
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book
Challenge 2

Uppgift

Swipe to start coding

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.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 5. Kapitel 4
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
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