Last Updated on August 5, 2019 You are handed data and told to develop a forecast model. What do you do? This is a common situation; far more common than most people think. Perhaps you are sent a CSV file. Perhaps you are given access to a database. Perhaps you are starting a competition. The problem can be reasonably well defined: You have or can access historical time series data. You know or can find out what needs to be […]

Last Updated on August 28, 2019 Time series data often requires some preparation prior to being modeled with machine learning algorithms. For example, differencing operations can be used to remove trend and seasonal structure from the sequence in order to simplify the prediction problem. Some algorithms, such as neural networks, prefer data to be standardized and/or normalized prior to modeling. Any transform operations applied to the series also require a similar inverse transform to be applied on the predictions. This […]

Last Updated on August 28, 2020 A Case Study in How to Avoid Methodological Errors whenEvaluating Machine Learning Methods for Time Series Forecasting. Evaluating machine learning models on time series forecasting problems is challenging. It is easy to make a small error in the framing of a problem or in the evaluation of models that give impressive results but result in an invalid finding. An interesting time series classification problem is predicting whether a subject’s eyes are open or closed […]

Last Updated on August 28, 2020 Small computers, such as Arduino devices, can be used within buildings to record environmental variables from which simple and useful properties can be predicted. One example is predicting whether a room or rooms are occupied based on environmental measures such as temperature, humidity, and related measures. This is a type of common time series classification problem called room occupancy classification. In this tutorial, you will discover a standard multivariate time series classification problem for […]