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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

ChallengeChallenge

Task

Create an autoregressive model to predict the dataset aapl.csv. After, print the results and the model error.

  1. Read the aapl.csv dataset.
  2. Create an autoregressive model (AutoReg) with 3 lags for the X data and assign it to the model variable.
  3. Fit model to the data and assign it to the model_fit variable.
  4. Predict the first 30 values.
  5. Visualize the results: display the first 30 values of X within the first call of the print() function, and first 30 values of the predictions within the second call.
  6. Calculate the RMSE (square root of the mean squared error) and display it.

Everything was clear?

Section 4. Chapter 5
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

ChallengeChallenge

Task

Create an autoregressive model to predict the dataset aapl.csv. After, print the results and the model error.

  1. Read the aapl.csv dataset.
  2. Create an autoregressive model (AutoReg) with 3 lags for the X data and assign it to the model variable.
  3. Fit model to the data and assign it to the model_fit variable.
  4. Predict the first 30 values.
  5. Visualize the results: display the first 30 values of X within the first call of the print() function, and first 30 values of the predictions within the second call.
  6. Calculate the RMSE (square root of the mean squared error) and display it.

Everything was clear?

Section 4. Chapter 5
toggle bottom row
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