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.

- Read the
`aapl.csv`

dataset. - Create an autoregressive model (
`AutoReg`

) with 3 lags for the`X`

data and assign it to the`model`

variable. - Fit model to the data and assign it to the
`model_fit`

variable. - Predict the first 30 values.
- 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. - Calculate the RMSE (square root of the mean squared error) and display it.

Everything was clear?

# Challenge

Task

Create an autoregressive model to predict the dataset `aapl.csv`

. After, print the results and the model error.

- Read the
`aapl.csv`

dataset. - Create an autoregressive model (
`AutoReg`

) with 3 lags for the`X`

data and assign it to the`model`

variable. - Fit model to the data and assign it to the
`model_fit`

variable. - Predict the first 30 values.
- 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. - Calculate the RMSE (square root of the mean squared error) and display it.

Everything was clear?