Last Updated on August 15, 2020 Our time series dataset may contain a trend. A trend is a continued increase or decrease in the series over time. There can be benefit in identifying, modeling, and even removing trend information from your time series dataset. In this tutorial, you will discover how to model and remove trend information from time series data in Python. After completing this tutorial, you will know: The importance and types of trends that may exist in time […]

Last Updated on August 15, 2020 Time series datasets can contain a seasonal component. This is a cycle that repeats over time, such as monthly or yearly. This repeating cycle may obscure the signal that we wish to model when forecasting, and in turn may provide a strong signal to our predictive models. In this tutorial, you will discover how to identify and correct for seasonality in time series data with Python. After completing this tutorial, you will know: The […]

Last Updated on August 21, 2019 Establishing a baseline is essential on any time series forecasting problem. A baseline in performance gives you an idea of how well all other models will actually perform on your problem. In this tutorial, you will discover how to develop a persistence forecast that you can use to calculate a baseline level of performance on a time series dataset with Python. After completing this tutorial, you will know: The importance of calculating a baseline […]

Last Updated on August 15, 2020 Moving average smoothing is a naive and effective technique in time series forecasting. It can be used for data preparation, feature engineering, and even directly for making predictions. In this tutorial, you will discover how to use moving average smoothing for time series forecasting with Python. After completing this tutorial, you will know: How moving average smoothing works and some expectations of your data before you can use it. How to use moving average […]

Last Updated on August 15, 2020 Time series is different from more traditional classification and regression predictive modeling problems. The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically. For example, when modeling, there are assumptions that the summary statistics of observations are consistent. In time series terminology, we refer to this expectation as the time series being stationary. These assumptions can be […]

Last Updated on August 15, 2020 Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems. In this tutorial, you will discover how to implement an autoregressive model for time series forecasting with Python. After completing this tutorial, you will know: How to […]

Last Updated on September 18, 2019 6 Ways to Plot Your Time Series Data with Python Time series lends itself naturally to visualization. Line plots of observations over time are popular, but there is a suite of other plots that you can use to learn more about your problem. The more you learn about your data, the more likely you are to develop a better forecasting model. In this tutorial, you will discover 6 different types of plots that you […]

Last Updated on September 18, 2019 Forecast errors on time series regression problems are called residuals or residual errors. Careful exploration of residual errors on your time series prediction problem can tell you a lot about your forecast model and even suggest improvements. In this tutorial, you will discover how to visualize residual errors from time series forecasts. After completing this tutorial, you will know: How to create and review line plots of residual errors over time. How to review […]

Last Updated on August 19, 2020 A popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a class of model that captures a suite of different standard temporal structures in time series data. In this tutorial, you will discover how to develop an ARIMA model for time series forecasting in Python. After completing this tutorial, you will know: About the ARIMA model […]

Last Updated on April 24, 2020 The residual errors from forecasts on a time series provide another source of information that we can model. Residual errors themselves form a time series that can have temporal structure. A simple autoregression model of this structure can be used to predict the forecast error, which in turn can be used to correct forecasts. This type of model is called a moving average model, the same name but very different from moving average smoothing. […]