SYSTEMS AND METHODS FOR TRAINING PREDICTIVE MODELS ON SEQUENTIAL DATA USING 1-DIMENSIONAL CONVOLUTIONAL LAYERS IN A BLIND LEARNING APPROACH
A system and method are disclosed related to building a predictive model from sequential data using convolutional neural networks such as predicting the remaining useful life of a system. An example method includes organizing training data into a two-dimensional format, normalizing the training data to yield normalized training data, simulating a sequence model using a one-dimensional convolutional neural network, collecting feature maps that result from previous layers in the one-dimensional convolutional neural network into a single layer, inputting an output from the single layer into a fully connected network and predicting, based on the fully connected network operating on the output of the single layer, a target value associated with the training data.
1 . A method comprising:
organizing training data into a two-dimensional format;
normalizing the training data to yield normalized training data;
simulating a sequence model using a one-dimensional convolutional neural network;
collecting feature maps that result from previous layers in the one-dimensional convolutional neural network into a single layer;
inputting an output from the single layer into a fully connected network; and
predicting, based on the fully connected network operating on the output of the single layer, a target value associated with the training data.
2 . The method of claim 1 , wherein the two-dimensional format comprises a first dimension in time and a second dimension representing a feature.
3 . The method of claim 1 , wherein the normalizing of the training data normalizes the training data into a range between and including [−1, 1].
4 . The method of claim 1 , wherein the one-dimensional convolutional neural network comprises a Conv1D convolutional neural network.
5 . The method of claim 1 , further comprising:
selecting a time window over the training data, wherein the time window covers a plurality of rows in the training data.
6 . The method of claim 5 , wherein the time window is one of static and dynamic.
7 . The method of claim 1 , wherein the training data comprises time series data.
8 . A method comprising:
organizing training data into a two-dimensional format;
normalizing the training data to yield normalized training data;
training a convolutional neural network on the normalized training data to yield a trained convolutional neural network; and
predicting, based on input data to the trained convolutional neural network, a target value associated with the training data.
9 . The method of claim 8 , wherein the training data comprises time series data.
10 . The method of claim 1 , further comprising:
selecting a time window over the training data, wherein the time window covers a plurality of rows in the training data.
11 . A system comprising:
a processor; and
a computer-readable storage device storing instructions which, when executed by the processor, cause the processor to perform operations comprising:
organizing training data into a two-dimensional format;
normalizing the training data to yield normalized training data;
simulating a sequence model using a one-dimensional convolutional neural network;
collecting feature maps that result from previous layers in the one-dimensional convolutional neural network into a single layer;
inputting an output from the single layer into a fully connected network; and
predicting, based on the fully connected network operating on the output of the single layer, a target value associated with the training data.
12 . The system of claim 11 , wherein the two-dimensional format comprises a first dimension in time and a second dimension representing a feature.
13 . The system of claim 11 , wherein the normalizing of the training data normalizes the training data into a range between and including [−1, 1].
14 . The system of claim 11 , wherein the one-dimensional convolutional neural network comprises a Conv1D convolutional neural network.
15 . The system of claim 11 , wherein the computer-readable storage device stores additional instructions which, when executed by the processor, cause the processor to perform operations further comprising:
selecting a time window over the training data, wherein the time window covers a plurality of rows in the training data.
16 . The system of claim 11 , wherein the time window is one of static and dynamic.
17 . The system of claim 11 , wherein the training data comprises time series data.
18 . The system of claim 11 , wherein normalizing the training data occurs using min-max normalization.
19 . The system of claim 11 , wherein a same padding is used to keep a size of the input data unchanged through the one-dimensional convolutional neural network.
20 . The system of claim 11 , wherein normalizing data further comprises normalizing to a negative lower value and a positive higher value.