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methodological error
dataset = read_csv('usdinr_dataset.csv', header=0, index_col=0)
values = dataset.values
values = values.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
.....u r scaling the FULL dataset here with minmax scaler instead of splitting dataset and putting away test to later scale using the transform coefficients derived from train set scaling.
Predicting using future data
You have a fundamental issue with your code: you are predicting values by looking at the future!!
Predictors["open_1"] = CNHI["data"].Open.shift(1)
Predictors["open_incr"] = CNHI["data"].Open - CNHI["data"].Open.shift(1)
One of the most salient features is the open_incr
, which is the difference between the open values of the current day and the value of the next row! You are essentially using the future as a predictor for the present: it makes no sense.
why "n_test = 277" ?
Hey Boris,
Might be a stupid question, may I ask why set it as 277, is there any special reason or experienced value?
Thanks and best regards!
Yunfei
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