Last Updated on August 14, 2020 White noise is an important concept in time series forecasting. If a time series is white noise, it is a sequence of random numbers and cannot be predicted. If the series of forecast errors are not white noise, it suggests improvements could be made to the predictive model. In this tutorial, you will discover white noise time series with Python. After completing this tutorial, you will know: The definition of a white noise time […]

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 […]

Last Updated on August 28, 2019 It is important to establish a strong baseline of performance on a time series forecasting problem and to not fool yourself into thinking that sophisticated methods are skillful, when in fact they are not. This requires that you evaluate a suite of standard naive, or simple, time series forecasting models to get an idea of the worst acceptable performance on the problem for more sophisticated models to beat. Applying these simple models can also […]

Last Updated on August 21, 2019 There are many parameters to consider when configuring an ARIMA model with Statsmodels in Python. In this tutorial, we take a look at a few key parameters (other than the order parameter) that you may be curious about. Specifically, after completing this tutorial, you will know: How to suppress noisy output from the underlying mathematical libraries when fitting an ARIMA model. The effect of enabling or disabling a trend term in your ARIMA model. […]

Last Updated on August 28, 2019 It is common to use persistence or naive forecasts as a first-cut forecast on time series problems. A better first-cut forecast on time series data with a seasonal component is to persist the observation for the same time in the previous season. This is called seasonal persistence. In this tutorial, you will discover how to implement seasonal persistence for time series forecasting in Python. After completing this tutorial, you will know: How to use […]

Last Updated on August 21, 2019 Machine learning methods like deep learning can be used for time series forecasting. Before machine learning can be used, time series forecasting problems must be re-framed as supervised learning problems. From a sequence to pairs of input and output sequences. In this tutorial, you will discover how to transform univariate and multivariate time series forecasting problems into supervised learning problems for use with machine learning algorithms. After completing this tutorial, you will know: How […]