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 […]

# Archive | Deep Learning for Time Series

## How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution

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 […]

## How to Develop Multi-Step Time Series Forecasting Models for Air Pollution

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 […]

## How to Develop Baseline Forecasts for Multi-Site Multivariate Air Pollution Time Series Forecasting

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 […]

## How to Load, Visualize, and Explore a Multivariate Multistep Time Series Dataset

## Multi-Step LSTM Time Series Forecasting Models for Power Usage

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 […]

## Convolutional Neural Networks for Multi-Step Time Series Forecasting

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 […]

## Multi-step Time Series Forecasting with Machine Learning for Electricity Usage

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 […]

## Autoregression Forecast Model for Household Electricity Consumption

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 […]

## Evaluate Naive Models for Forecasting Household Electricity Consumption

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. In this tutorial, you […]