Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge 1 | Non-Stationary Models
Time Series Analysis

Challenge 1Challenge 1

Task

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).

Everything was clear?

Section 5. Chapter 3
toggle bottom row
course content

Course Content

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

Time Series Analysis

Challenge 1Challenge 1

Task

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).

Everything was clear?

Section 5. Chapter 3
toggle bottom row
some-alt