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 […]
Archive | Deep Learning for Time Series
Deep Learning Models for Human Activity Recognition
Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model. Recently, deep learning methods […]
LSTMs for Human Activity Recognition Time Series Classification
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]
1D Convolutional Neural Network Models for Human Activity Recognition
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]
Evaluate Machine Learning Algorithms for Human Activity Recognition
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]
How to Model Human Activity From Smartphone Data
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to […]
A Gentle Introduction to a Standard Human Activity Recognition Problem
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to […]
Indoor Movement Time Series Classification with Machine Learning Algorithms
Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building. It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior. The ‘indoor user movement‘ dataset is a […]
Probabilistic Forecasting Model to Predict Air Pollution Days
Air pollution is characterized by the concentration of ground ozone. From meteorological measurements, such as wind speed and temperature, it is possible to forecast whether the ground ozone will be at a sufficiently high level tomorrow to issue a public air pollution warning. This is the basis behind a standard machine learning dataset used for […]
How to Get Started with Deep Learning for Time Series Forecasting (7-Day Mini-Course)
Deep Learning for Time Series Forecasting Crash Course. Bring Deep Learning methods to Your Time Series project in 7 Days. Time series forecasting is challenging, especially when working with long sequences, noisy data, multi-step forecasts and multiple input and output variables. Deep learning methods offer a lot of promise for time series forecasting, such as […]