Data analysis using jupyter notebook -- 2

1.Focus now on the AMZN stock volume time series.
a. Split the data into training and testing series, selecting the testing series to be the
last three months of the data.
b. Manually step through the STR decomposition process on the training data, as
described in the course material. Visualise and interpret each of the components of
the STR decomposition for volume. (Hint: You may wish to validate the output of
your manual process against an automated modelling approach.)
2. Timeseries models:
a. Fit an ARIMA model for the trend-cycle component of your STR decomposition of
the training data and interpret the estimated model parameters.
b. Using the STR components that you estimated in tasks 3 and 4, produce forecasts of
AMZN stock volumes for the test data series. Include the uncertainty in the forecasts
and visualise the predictions.
3. Pure forecasters - now consider your choice of ML techniques:
a. Select an appropriate pure forecasting method to predict the trend component of
the volume training data.
b. Using the seasonal component that you estimated in task 3 and the pure forecaster
from 5.a, produce forecasts of AMZN stock volumes for the test data series. Include
the uncertainty in the forecasts, and visualise the predictions.
4. Evaluate the forecast performance of your model-based and pure forecasters using the test
data and compare the two forecasters. Use appropriate evaluation metrics and methods.
Discuss the similarities and difference between their performance and suggest possible
avenues for improvement.