The term forecasting is most often applied to time series data, where future values of a series are estimated based on past and present observations. Usually you need to build a model to describe how the variable(s) in interest changes with respective to time or other possible factors based on history data.
Here’s a list of all relevant libraries for Time Series Forecasting. Good to have these gems in your bucket: 📚skforecast Skforecast is a Python library that eases using scikit-learn regressors as single and multi-step forecasters. It also works with any regressor…
Machine learning is a powerful tool that is transforming the way businesses operate. By using algorithms and statistical models, businesses can make more accurate predictions and better understand their customers and operations. One of the main benefits of machine learning…
Introduction Time-series forecasting is a powerful tool that allows businesses to make informed decisions about the future. By analyzing past data and trends, businesses can make predictions about future events and plan accordingly. Time-series forecasting is commonly used in many…
Time series analysis is the process of gathering data over a period of time at regular intervals with the aim of discovering trends, seasonality, and residuals to help with event forecasting. Time series analysis entails attempting to anticipate future values…
What is Backtesting? Backtesting is the process of testing a model against historical data. By simulating past data conditions, you can ensure that your model is accurate. Think of it as cross-validation for time-series models. Why is the backtesting of…
When doing time series forecasting, it is important to have a good understanding of the external data that will be used to predict future events. This understanding can be gained by looking at past data and analyzing how it relates…