Last Updated on August 28, 2019 Some machine learning algorithms will achieve better performance if your time series data has a consistent scale or distribution. Two techniques that you can use to consistently rescale your time series data are normalization and standardization. In this tutorial, you will discover how you can apply normalization and standardization rescaling to your time series data in Python. After completing this tutorial, you will know: The limitations of normalization and expectations of your data for […]

Last Updated on September 15, 2019 Time Series data must be re-framed as a supervised learning dataset before we can start using machine learning algorithms. There is no concept of input and output features in time series. Instead, we must choose the variable to be predicted and use feature engineering to construct all of the inputs that will be used to make predictions for future time steps. In this tutorial, you will discover how to perform feature engineering on time […]

Last Updated on February 11, 2020 You may have observations at the wrong frequency. Maybe they are too granular or not granular enough. The Pandas library in Python provides the capability to change the frequency of your time series data. In this tutorial, you will discover how to use Pandas in Python to both increase and decrease the sampling frequency of time series data. After completing this tutorial, you will know: About time series resampling, the two types of resampling, […]

Last Updated on August 28, 2019 k-fold Cross Validation Does Not Work For Time Series Data andTechniques That You Can Use Instead. The goal of time series forecasting is to make accurate predictions about the future. The fast and powerful methods that we rely on in machine learning, such as using train-test splits and k-fold cross validation, do not work in the case of time series data. This is because they ignore the temporal components inherent in the problem. In […]

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 […]