starter-code-16.ipynb

Kernel: Python 2

import pandas as pd
import numpy as np

%matplotlib inline

Walmart Sales Data

For the independent practice, we will analyze the weekly sales data from Walmart over a two year period from 2010 to 2012.

The data is again separated by store and by department, but we will focus on analyzing one store for simplicity.

The data includes:

• Store - the store number

• Dept - the department number

• Date - the week

• Weekly_Sales - sales for the given department in the given store

• IsHoliday - whether the week is a special holiday week

Loading the data and setting the DateTimeIndex

data = pd.read_csv('../../assets/dataset/train.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)
data.head()

Filter the dataframe to Store 1 sales and aggregate over departments to compute the total sales per store.

# TODO

Plot the rolling_mean for Weekly_Sales. What general trends do you observe?

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Compute the 1, 2, 52 autocorrelations for Weekly_Sales and/or create an autocorrelation plot.

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Split the weekly sales data in a training and test set - using 75% of the data for training

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Create an AR(1) model on the training data and compute the mean absolute error of the predictions.

import statsmodels.api as sm
from sklearn.metrics import mean_absolute_error

# TODO

Plot the residuals - where are their significant errors.

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Compute and AR(2) model and an ARMA(2, 2) model - does this improve your mean absolute error on the held out set.

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Finally, compute an ARIMA model to improve your prediction error - iterate on the p, q, and parameters comparing the model's performance.

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