# Assignment #9 (demo). Time series analysis¶

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.

Same assignment as a Kaggle Notebook + 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
import requests
from plotly import __version__
from plotly import graph_objs as go

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

5.4.0

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)


## Data preparation¶

# for Jupyter-book, we copy data from GitHub, locally, to save Internet traffic,
# you can specify the data/ folder from the root of your cloned
# https://github.com/Yorko/mlcourse.ai repo, to save Internet traffic
DATA_PATH = "https://raw.githubusercontent.com/Yorko/mlcourse.ai/master/data/"

df = pd.read_csv(DATA_PATH + "wiki_machine_learning.csv", sep=" ")
df = df[df["count"] != 0]

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 prophet 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 (read-only in a JupyterBook, pls run jupyter-notebook to edit)


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

# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)


Question 2: What is MAPE equal to?

• 34.5

• 42.42

• 5.39

• 65.91

# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)


Question 3: What is MAE equal to?

• 355

• 4007

• 600

• 903

# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)


## Predicting with ARIMA¶

%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.api as sm
from scipy import stats

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 (read-only in a JupyterBook, pls run jupyter-notebook to edit)


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 (read-only in a JupyterBook, pls run jupyter-notebook to edit)