Search results for "Machine Learning"

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. […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

Time series forecasting is an important area of machine learning that is often neglected. It is important because there are so many prediction problems that involve a time component. These problems are neglected because it is this time component that makes time series problems more difficult to handle. In this post, you will discover time […]