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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 1

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Time for new challenges! Here is the first challenge, the idea of which is to process the pr_HH Spot Price.csv dataset to turn it from non-stationary to stationary:

  1. Read the dataset.
  2. Test for data stationarity (use adfuller) and display results.
  3. Visualize the initial values of the "Price" column.
  4. Transform data (the "Price" column of the df DataFrame) from non-stationary to stationary using the difference method (using the .diff() method with periods = 1 parameter). Drop NA values. Assign the result to the new_diff variable.
  5. Visualize the modified data (new_diff).
  6. Rerun the ADF test for updated data (new_diff).

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 3
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book
Challenge 1

Uppgift

Swipe to start coding

Time for new challenges! Here is the first challenge, the idea of which is to process the pr_HH Spot Price.csv dataset to turn it from non-stationary to stationary:

  1. Read the dataset.
  2. Test for data stationarity (use adfuller) and display results.
  3. Visualize the initial values of the "Price" column.
  4. Transform data (the "Price" column of the df DataFrame) from non-stationary to stationary using the difference method (using the .diff() method with periods = 1 parameter). Drop NA values. Assign the result to the new_diff variable.
  5. Visualize the modified data (new_diff).
  6. Rerun the ADF test for updated data (new_diff).

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 3
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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