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Challenge | Stationary Models
Time Series Analysis

Course Content

Time Series Analysis

## Time Series Analysis

1. Time Series: Let's Start

2. Time Series Processing

3. Time Series Visualization

# Challenge

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

# Challenge

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
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