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Time Series Analysis

Challenge 2Challenge 2

Task

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.

Everything was clear?

Section 5. Chapter 4
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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 2Challenge 2

Task

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.

Everything was clear?

Section 5. Chapter 4
toggle bottom row
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