We have time-series data kept in the file /cxldata/pet_mle/time_series_data.csv
This is the stock data over a period of time. It is stored chronologically - the first record is older than second, second is older than third and so on. Do not shuffle it.
We want to create a dataset on which we can train a model to predict the stock price given the previous 5 values. So, we have to convert it into a dataset such that the previous 5 values are the features and the 6th value is the label.
If our input dataset is: t1, t2, t3, t4, t5, t6, t7, t8, t9, t10
Our expected X is:
[
[t1, t2, t3, t4, t5] ,
[t2, t3, t4, t5, t6] ,
[t3, t4, t5, t6, t7] ,
[t4, t5, t6, t7, t8] ,
[t5, t6, t7, t8, t9] ,
[t6, t7, t8, t9, t10] ,
]
Our expect y is: [t6, t7, t8, t9, t10]
import pandas as pd and import numpy as npX and y as described above.X should be two dimensional and y should a single-dimensional NumPy arrayNote - Having trouble with the assessment engine? Follow the steps listed here
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