Last Updated on August 28, 2020 Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Autocorrelation models are very simple and can provide a fast and effective way to make skillful one-step and multi-step forecasts for electricity consumption. […]

Last Updated on August 28, 2020 Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Machine learning algorithms predict a single value and cannot be used directly for multi-step forecasting. Two strategies that can be used to make […]

Last Updated on August 28, 2020 Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Unlike other machine learning algorithms, convolutional neural networks are capable of automatically learning features from sequence data, support multiple-variate data, and can directly […]

Last Updated on August 28, 2020 Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Unlike other machine learning algorithms, long short-term memory recurrent neural networks are capable of automatically learning features from sequence data, support multiple-variate data, […]

Last Updated on August 5, 2019 Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality Prediction‘ dataset for short, describes weather conditions at multiple sites and requires a prediction of air quality measurements over […]

Last Updated on August 28, 2020 Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality Prediction‘ dataset for short, describes weather conditions at multiple sites and requires a prediction of air quality measurements over […]

Last Updated on August 28, 2020 Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality Prediction’ dataset for short, describes weather conditions at multiple sites and requires a prediction of air quality measurements over […]

Last Updated on August 28, 2020 Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality Prediction’ dataset for short, describes weather conditions at multiple sites and requires a prediction of air quality measurements over […]

Last Updated on August 28, 2020 Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component. It is common practice to use an optimization process to find the model hyperparameters that result in the exponential smoothing model with the best performance for a given time series dataset. This practice applies only to the coefficients used by the model to describe the exponential structure of the […]

Last Updated on August 28, 2020 The Seasonal Autoregressive Integrated Moving Average, or SARIMA, model is an approach for modeling univariate time series data that may contain trend and seasonal components. It is an effective approach for time series forecasting, although it requires careful analysis and domain expertise in order to configure the seven or more model hyperparameters. An alternative approach to configuring the model that makes use of fast and parallel modern hardware is to grid search a suite […]