mlcourse.ai – Open Machine Learning Course

Author: Mariya Mansurova, Analyst & developer in Yandex.Metrics team. Translated by Ivan Zakharov, ML enthusiast.
This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.

Assignment #9 (demo)

Time series analysis

Same assignment as a Kaggle Kernel + solution.

Fill cells marked with "Your code here" and submit your answers to the questions through the web form.

import os
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd

from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly import graph_objs as go
import requests

print(__version__) # need 1.9.0 or greater
init_notebook_mode(connected = True)


def plotly_df(df, title = ''):
    data = []
    
    for column in df.columns:
        trace = go.Scatter(
            x = df.index,
            y = df[column],
            mode = 'lines',
            name = column
        )
        data.append(trace)
    
    layout = dict(title = title)
    fig = dict(data = data, layout = layout)
    iplot(fig, show_link=False)

3.2.1

Data preparation

df = pd.read_csv('../../data/wiki_machine_learning.csv', sep = ' ')
df = df[df['count'] != 0]
df.head()

	date	count	lang	page	rank	month	title
81	2015-01-01	1414	en	Machine_learning	8708	201501	Machine_learning
80	2015-01-02	1920	en	Machine_learning	8708	201501	Machine_learning
79	2015-01-03	1338	en	Machine_learning	8708	201501	Machine_learning
78	2015-01-04	1404	en	Machine_learning	8708	201501	Machine_learning
77	2015-01-05	2264	en	Machine_learning	8708	201501	Machine_learning

df.shape

(383, 7)

Predicting with FB Prophet

We will train at first 5 months and predict the number of trips for June.

df.date = pd.to_datetime(df.date)

plotly_df(df.set_index('date')[['count']])

from fbprophet import Prophet

predictions = 30

df = df[['date', 'count']]
df.columns = ['ds', 'y']
df.tail()

	ds	y
382	2016-01-16	1644
381	2016-01-17	1836
376	2016-01-18	2983
375	2016-01-19	3389
372	2016-01-20	3559

Question 1: What is the prediction of the number of views of the wiki page on January 20? Round to the nearest integer.

• 4947
• 3426
• 5229
• 2744

# You code here

Estimate the quality of the prediction with the last 30 points.

# You code here

Question 2: What is MAPE equal to?

• 34.5
• 42.42
• 5.39
• 65.91

Question 3: What is MAE equal to?

• 355
• 4007
• 600
• 903

Predicting with ARIMA

%matplotlib inline
import matplotlib.pyplot as plt
from scipy import stats
import statsmodels.api as sm
plt.rcParams['figure.figsize'] = (15, 10)

Question 4: Let's verify the stationarity of the series using the Dickey-Fuller test. Is the series stationary? What is the p-value?

• Series is stationary, p_value = 0.107
• Series is not stationary, p_value = 0.107
• Series is stationary, p_value = 0.001
• Series is not stationary, p_value = 0.001

# You code here

Next, we turn to the construction of the SARIMAX model (sm.tsa.statespace.SARIMAX).
Question 5: What parameters are the best for the model according to the AIC criterion?

• D = 1, d = 0, Q = 0, q = 2, P = 3, p = 1
• D = 2, d = 1, Q = 1, q = 2, P = 3, p = 1
• D = 1, d = 1, Q = 1, q = 2, P = 3, p = 1
• D = 0, d = 0, Q = 0, q = 2, P = 3, p = 1

# You code here