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(assignment09)=
# Assignment #9 (demo). Time series analysis
**[mlcourse.ai](https://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](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose.
**Same assignment as a [Kaggle Notebook](https://www.kaggle.com/kashnitsky/a9-demo-time-series-analysis) + [solution](https://www.kaggle.com/kashnitsky/a9-demo-time-series-analysis-solution).**
**In this assignment, we are using Prophet and ARIMA to analyze the number of views for a Wikipedia [page](https://en.wikipedia.org/wiki/Machine_learning) on Machine Learning.**
**Fill cells marked with "Your code here" and submit your answers to the questions through the [web form](https://docs.google.com/forms/d/1UYQ_WYSpsV3VSlZAzhSN_YXmyjV7YlTP8EYMg8M8SoM/edit).**
```{code-cell} ipython3
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
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot, plot
from IPython.display import display, IFrame
print(__version__) # need 1.9.0 or greater
init_notebook_mode(connected=True)
```
```{code-cell} ipython3
def plotly_df(df, title="", width=800, height=500):
"""Visualize all the dataframe columns as line plots."""
common_kw = dict(x=df.index, mode="lines")
data = [go.Scatter(y=df[c], name=c, **common_kw) for c in df.columns]
layout = dict(title=title)
fig = dict(data=data, layout=layout)
# in a Jupyter Notebook, the following should work
#iplot(fig, show_link=False)
# in a Jupyter Book, we save a plot offline and then render it with IFrame
plot_path = f"../../_static/plotly_htmls/{title}.html".replace(" ", "_")
plot(fig, filename=plot_path, show_link=False, auto_open=False);
display(IFrame(plot_path, width=width, height=height))
```
## Data preparation
```{code-cell} ipython3
# 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/main/data/"
```
```{code-cell} ipython3
df = pd.read_csv(DATA_PATH + "wiki_machine_learning.csv", sep=" ")
df = df[df["count"] != 0]
df.head()
```
```{code-cell} ipython3
df.shape
```
## Predicting with FB Prophet
We will train on the first 5 months and predict the number of trips for June.
```{code-cell} ipython3
df.date = pd.to_datetime(df.date)
```
```{code-cell} ipython3
plotly_df(df=df.set_index("date")[["count"]], title="assign9_plot")
```
```{code-cell} ipython3
from prophet import Prophet
```
```{code-cell} ipython3
predictions = 30
df = df[["date", "count"]]
df.columns = ["ds", "y"]
df.tail()
```
**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
```{code-cell} ipython3
# 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.
```{code-cell} ipython3
# 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
```{code-cell} ipython3
# 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
```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```
## Predicting with ARIMA
```{code-cell} ipython3
%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
```{code-cell} ipython3
# 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
```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```