Hello Isabelle,
Based on your description, I think you're seeking the correct method for dividing your time series data into training, testing, and validation sets. I can share an effective approach that I have personally utilized.
- You've mentioned having 10 observations, with each one comprising both input and output data. Specifically, the input data consists of a time series sequence of 500 steps with 3 features, and the output data is a sequence of 500 steps for a single variable. Therefore, your data should be organized as 1x10 sequences within a cell array, where each sequence is represented as a list of 500x4, including 3 inputs and 1 output.
- To partition this data into training, testing, and validation sets, you can use the cvpartition function. However, it's important to note that cvpartition generates two sets at a time, necessitating its use twice. Initially, divide the data into a training set and a combined testing/validation set. Subsequently, split the latter into distinct testing and validation sets. After this the whole trainData would contain 8 sequences(80 percent) and validate and test would contain 1 sequence each (10 percent each).
- Once partitioned, proceed to organize the training data into Xtrain, which comprises the input sequences of 500x3, and Ytrain, which includes the output sequences of 500x1.
Please go through the following documentation to learn more,
Hope this helps.
