Last Updated on August 5, 2019 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 the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. In this […]

Last Updated on August 28, 2020 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 time series classification dataset, called simply the “ozone prediction problem“. This dataset describes meteorological observations over seven years in […]

Last Updated on August 28, 2020 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 standard and freely available time series classification problem. In this tutorial, you will discover the indoor movement prediction time […]

Last Updated on August 5, 2019 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 known movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-size […]

Last Updated on August 5, 2019 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 known movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized […]

Last Updated on August 28, 2020 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 that this feature engineering requires deep expertise in the field. Recently, deep learning methods such as recurrent neural networks […]

Last Updated on August 28, 2020 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 that this feature engineering requires deep expertise in the field. Recently, deep learning methods such as recurrent neural networks […]

Last Updated on August 28, 2020 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 that this feature engineering requires strong expertise in the field. Recently, deep learning methods such as recurrent neural networks […]