Tackling TSC with K-nearest neighbors

In this recipe, we’ll show you how to tackle TSC tasks using a popular method called K-nearest neighbors. The goal of this recipe is to show you how standard machine-learning models can be used to solve this problem.

Getting ready

First, let’s start by loading the data using pandas:

import pandas as pd
data_directory = 'assets/datasets/Car'
train = pd.read_table(f'{data_directory}/Car_TRAIN.tsv', header=None)
test = pd.read_table(f'{data_directory}/Car_TEST.tsv', header=None)

The dataset is already split into a training and testing set, so we read them separately. Now, let’s see how to build a K-nearest neighbor model using this dataset.

How to do it…

Here, we describe the steps necessary for building a time series classifier using scikit-learn:

1. Let’s start by splitting the target variable from the explanatory variables:

   y_train = train.iloc[:, 0]
   y_test =...