How to use scikit-learn with time series data?

You might have heard of scikit-learn, as a data scientist or machine learning engineer. Scikit-learn is a library used for various supervised and unsupervised machine learning algorithms. In this blog post, we will cover how to handle time series data and use scikit-learn for time series forecasting.

All data science end-to-end projects start off with the preparation of data. The preparation of data involves cleaning the dataset and also using techniques like imputation for any missing values. Another important step in time series forecasting is the removal of outliers as well. Once the dataset has been cleaned, the next step is to use any specific machine learning algorithm on time series dataset. There are various time series algorithms that can be used for instance:

1. ARIMA: Arima models are specifically meant to be used for time series forecasting.

2. Exponential smoothing: Exponential smoothing is another type of statistical model used for time series forecasting.

3. Prophet: Facebook Prophet is a forecasting tool also used for time series forecasting.

4. Recurrent Neural Networks: RNNs are a type of deep learning neural network used for time series forecasting

Once you have chosen a machine learning model, the next step is to train the machine learning model on the dataset and then use the test dataset to make predictions. One example of using scikit-learn for time series forecasting can be seen in the code snippet below:

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Load the time series data
df = pd.read_csv('time_series_data.csv')

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(df['index'], df['value'], test_size=0.25)

# Create a linear regression model
model = LinearRegression()

# Train the model on the training data
model.fit(X_train[:, np.newaxis], y_train)

# Make predictions on the test data
y_pred = model.predict(X_test[:, np.newaxis])

# Evaluate the model's performance
mse = np.mean((y_test - y_pred)**2)
print('MSE:', mse)

One thing that needs to be remembered is that choosing the right machine learning model is important for time series forecasting.

References

https://cienciadedatos.net/documentos/py27-time-series-forecasting-python-scikitlearn.html