# π Introductory exercise for non i.i.d. dataΒΆ

Note

i.i.d is the acronym of βindependent and identically distributedβ (as in βindependent and identically distributed random variablesβ).

This exercise aims at showing some aspects to consider when dealing with non i.i.d data, typically time series.

For this purpose, we will create a synthetic dataset simulating stock values. We will formulate the following data science problem: predict the value of a specific stock given other stock.

To make this problem interesting, we want to ensure that any predictive model should not work. In this regard, the stocks will be generated completely randomly without any link between them. We will only add a constraint: the value of a stock at a given time t will depend on the value of the stock from the past.

We will create a function to generate such data.

import numpy as np
import pandas as pd

def generate_random_stock_market(n_stock=3, seed=0):
rng = np.random.RandomState(seed)

date_range = pd.date_range(start="01/01/2010", end="31/12/2020")
stocks = np.array([
rng.randint(low=100, high=200) +
np.cumsum(rng.normal(size=(len(date_range),)))
for _ in range(n_stock)
]).T
return pd.DataFrame(
stocks,
columns=[f"Stock {i}" for i in range(n_stock)],
index=date_range,
)


Now that we have our data generator, letβs create three quotes, corresponding to the quotes of three different companies for instance. We will plot the stock values

stocks = generate_random_stock_market()

Stock 0 Stock 1 Stock 2
2010-01-01 145.122795 121.931662 190.469167
2010-01-02 145.425600 120.771145 190.261145
2010-01-03 145.496459 122.281423 191.566923
2010-01-04 145.569501 122.174231 191.399200
2010-01-05 144.147175 123.107346 189.082024
import matplotlib.pyplot as plt

stocks.plot()
plt.ylabel("Stock value")
plt.legend(bbox_to_anchor=(1.05, 0.8), loc="upper left")
_ = plt.title("Stock values over time")


Because the stocks are generated randomly, it is not possible for a predictive model to be able to predict the value of a stock depending on the other stocks. By using the cross-validation framework from the previous exercise, we will check that we get such expected results.

First, letβs organise our data into a matrix data and a vector target. Split the data such that the Stock 0 is the stock that we want to predict and the Stock 1 and Stock 2 are stocks used to build our model.

# Write your code here.


Letβs create a machine learning model. We can use a GradientBoostingRegressor.

# Write your code here.


Now, we have to define a cross-validation strategy to evaluate our model. Use a ShuffleSplit cross-validation.

# Write your code here.


We should be set to make our evaluation. Call the function cross_val_score to compute the $$R^2$$ score for the different split and report the mean and standard deviation of the model.

# Write your code here.


Your model is not giving random predictions. Could you ellaborate on what are the reasons of such a success on random data.