Last Updated on August 21, 2019 Time series forecasting is typically discussed where only a one-step prediction is required. What about when you need to predict multiple time steps into the future? Predicting multiple time steps into the future is called multi-step time series forecasting. There are four main strategies that you can use for multi-step forecasting. In this post, you will discover the four main strategies for multi-step time series forecasting. After reading this post, you will know: The […]

Last Updated on April 24, 2020 From Developer to Time Series Forecaster in 7 Days. Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling time series forecasting projects using Python in 7 days. This is a big and important post. You might want to bookmark it. Kick-start your project with my new book Time Series Forecasting With Python, including […]

Last Updated on August 28, 2019 Making out-of-sample forecasts can be confusing when getting started with time series data. The statsmodels Python API provides functions for performing one-step and multi-step out-of-sample forecasts. In this tutorial, you will clear up any confusion you have about making out-of-sample forecasts with time series data in Python. After completing this tutorial, you will know: How to make a one-step out-of-sample forecast. How to make a multi-step out-of-sample forecast. The difference between the forecast() and […]

Last Updated on August 28, 2019 How much history is required for a time series forecast model? This is a problem-specific question that we can investigate by designing an experiment. In this tutorial, you will discover the effect that history size has on the skill of an ARIMA forecast model in Python. Specifically, in this tutorial, you will: Load a standard dataset and fit an ARIMA model. Design and execute a sensitivity analysis of the number of years of historic […]

Last Updated on September 16, 2020 The use of machine learning methods on time series data requires feature engineering. A univariate time series dataset is only comprised of a sequence of observations. These must be transformed into input and output features in order to use supervised learning algorithms. The problem is that there is little limit to the type and number of features you can engineer for a time series problem. Classical time series analysis tools like the correlogram can […]