A model of neural network to recognize spatiotemporal patterns is presented. Two examples of spatiotemporal pattern recognition are presented. They are achieved by giving different values to the parameters of the network. In one example, the network recognizes both directional and non-directional patterns.
What is spatio-temporal data mining?
Spatiotemporal data mining refers to the process of discovering patterns and knowledge from spatiotemporal data. Other examples of moving-object data mining include mining periodic patterns for one or a set of moving objects, and mining trajectory patterns, clusters, models, and outliers.
What is spatio-temporal forecasting?
Spatio-temporal wind forecasting methods are mainly based on the fact that the wind speed at a given site can be spatially correlated with those at other sites and this relation might be exploited to improve the forecasting accuracy compared to the models using only temporal data.
What is temporal data in machine learning?
Modeling temporal data is one of the fundamental tasks in machine learning since many systems keep track of signals that are changing over time. One example is using historical power consuption to forecast the future consumption – a crucial task for smart grids.
What is the definition of spatiotemporal?
Spatiotemporal, or spatial temporal, relates to space and time. Spatial refers to space and temporal refers to time.
What are temporal patterns?
Definition of temporal pattern : the unitary impression produced by a succession of stimuli (as in a melody or rhythm)
What is spatiotemporal gait?
Spatiotemporal gait parameters based on the recorded footfalls were measured during steady-state walking using the GAITRite®-system. This gait system is an electronic walkway with an integrated pressure-sensitive electronic surface connected to a portable computer via an interface cable.
What is ConvLSTM?
ConvLSTM is a type of recurrent neural network for spatio-temporal prediction that has convolutional structures in both the input-to-state and state-to-state transitions. The ConvLSTM determines the future state of a certain cell in the grid by the inputs and past states of its local neighbors.
What is temporal prediction?
Temporal prediction (TP): A joint TM/OF scheme is used to establish the correspondence of the object region between two consecutive frames.
What is the meaning of temporal data?
Temporal data is simply data that represents a state in time, such as the land-use patterns of Hong Kong in 1990, or total rainfall in Honolulu on July 1, 2009. Temporal data is collected to analyze weather patterns and other environmental variables, monitor traffic conditions, study demographic trends, and so on.
What is temporal convolutional?
Temporal convolutional network (TCN) is a framework which employs casual convolutions and dilations so that it is adaptive for sequential data with its temporality and large receptive fields.
