Forecast single and multiple time series with machine learning models like linear regression, random forests and xgboost. Implement backtesting to evaluate models before deployment.
Forecast single and multiple time series with regression models.
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Kishan is a machine learning and data science lead, course instructor, and open source software contributor. He has contributed to well known Python packages including Statsmodels, Feature-engine, and Prophet. He presents at data science conferences including ODSC and PyData. Kishan attained a PhD in Physics from Imperial College London in applied large scale time-series analysis and modelling of cardiac arrhythmias; during this time he taught and supervised undergraduates and master's students.
Sole is a lead data scientist, instructor and developer of open source software. She created and maintains the Python library Feature-engine, which allows us to impute data, encode categorical variables, transform, create and select features.
Sole is also the author of the "Python Feature engineering Cookbook" by Packt editorial.
Sole's been recognized as one of Linkedin's voices in data science in 2024.
Welcome to the course “Time Series Forecasting with Machine Learning.” In this course, you will learn how to forecast multiple time series by using traditional machine learning algorithms like linear regression, decision trees, random forests and gradient boosting machines.
Forecasting is the process of predicting future values of a time series based on historical data. Traditionally, we’ve used statistical methods like ARIMA, SARIMA or exponential smoothing for forecasting. These forecasting models take a time series as input, and return a time series as output. They are simple, explainable, don’t require a lot of data preprocessing, and in many cases, they make accurate predictions.
We can also forecast with deep learning models like recurrent neural networks (RNNs) or long short-term memory LSTMs. However, while these models are powerful in some cases, neural networks require huge datasets to offer a significant performance improvement respect to simpler models. For real world use cases like demand forecasting or forecasting air pollution concentration, where data is limited, we can use simpler algorithms, that are faster to train and easier to explain.
In recent years, there’s been a growing trend in using traditional machine learning models, such as xgboost and linear regression, to do forecasting. These machine learning methods have been shown to be effective in dealing with multiple time series, which are often enriched with variables from additional datasets, and where it is highly desirable to learn across all of our data simultaneously. Indeed, models such as LightGBM have been shown to be highly effective at large-scale time series forecasting.
