Time Series Forecasting with Machine Learning

Time Series Forecasting is the process of predicting future values of a time-dependent variable based on its past values. Time series data is prevalent in various domains, including finance, economics, healthcare, and many others. Machine learning techniques are gaining popularity in time series forecasting for their ability to handle non-linear relationships between the input features and target variable. In this blog post, we will discuss the process of time series forecasting with machine learning and the different techniques that we can use to make predictions.

1. Data Preprocessing

The first step in any machine learning process is to preprocess the data properly. In the case of time series data, we need to ensure that the data is stationary. Stationarity refers to the statistical properties of a time series that remain constant over time. That is, the mean, variance, and covariance of a time series should be constant over time. If the time series data is not stationary, we need to transform it into a stationary time series through differencing.

import pandas as pd import numpy as np

Load the data

df = pd.read_csv(‘dataset.csv’)

Make the time series stationary

df[‘Diff’] = df[‘Value’].diff() df.dropna(inplace=True)

2. Feature Extraction

After making the time series stationary, we need to extract relevant features that will help us make predictions. Some of the commonly used features for time series forecasting include:

• Lag features: These are the past values of the time series that we use as input features for predicting future values.
• Moving averages: These are the rolling mean values over a specified time frame.
• Rolling standard deviation: These are the rolling standard deviation values over a specified time frame.

Extract the features

df[‘Lag1’] = df[‘Diff’].shift(1) df[‘MA7’] = df[‘Value’].rolling(window=7).mean() df[‘MA30’] = df[‘Value’].rolling(window=30).mean() df[‘STD30’] = df[‘Value’].rolling(window=30).std() df.dropna(inplace=True) X = df[[‘Lag1’, ‘MA7’, ‘MA30’, ‘STD30’]] y = df[‘Value’]

3. Model Selection

Once we have extracted the features, we need to select the appropriate model for making predictions. There are various machine learning models that we can use for time series forecasting, including:

• Linear Regression: A popular linear model that can handle both linear and non-linear relationships between the features and the target variable.
• ARIMA: A time series model that can model the autocorrelation, seasonality, and trend in the time series data.
• Prophet: A time series model developed by Facebook that can model seasonality, trends, and holidays in the time series data.

Train the linear regression model

from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y)

4. Model Evaluation

After training the model, we need to evaluate its performance on the test data. We can use various metrics to evaluate the performance of the model, including:

• Mean Absolute Error (MAE): The average absolute difference between the predicted and actual values.
• Mean Squared Error (MSE): The average squared difference between the predicted and actual values.
• Root Mean Squared Error (RMSE): The square root of MSE.

Evaluate the model

from sklearn.metrics import mean_absolute_error, mean_squared_error y_pred = model.predict(X_test) print(‘MAE:’, mean_absolute_error(y_test, y_pred)) print(‘MSE:’, mean_squared_error(y_test, y_pred)) print(‘RMSE:’, np.sqrt(mean_squared_error(y_test, y_pred)))

5. Future Forecasting

After evaluating the model, we can use it to make predictions for future time periods. To make future predictions, we need to use the last few observed values of the time series to predict the next value.

Make future predictions

last_row = df.tail(1) last_row.drop(‘Value’, axis=1, inplace=True) future_values = model.predict(last_row)

Conclusion

Time series forecasting with machine learning is a complex yet crucial process for many applications. In this blog post, we discussed the process of time series forecasting with machine learning, including data preprocessing, feature extraction, model selection, model evaluation, and future forecasting. We also explored the various machine learning models that we can use for time series forecasting, including linear regression, ARIMA, and Prophet. By following these steps, we can make accurate and reliable predictions for time series data.

Additional Resources

• Time Series Analysis in Python: A Comprehensive Guide with Examples
• Practical Time Series Forecasting with Python
• Machine Learning Mastery - Time Series Forecasting