your code in main.py line189, the code of getting data is below, why do you use companies' stock price to predict NASDAQ-100 Index?
raw_data = pd.read_csv(os.path.join("data", "nasdaq100_padding.csv"), nrows=100 if debug else None)
logger.info(f"Shape of data: {raw_data.shape}.\nMissing in data: {raw_data.isnull().sum().sum()}.")
targ_cols = ("NDX",)
data, scaler = preprocess_data(raw_data, targ_cols)
NDX should be calculated by these stock prices, isn’t it? why u have to learn the calculation formula by RNN?
The DA-RNN paper gives a time series predicting model, right? But where is your time series predicting? I am confusion.
That's what I found when I read the code repeatedly, If I got wrong or missed something, please tell me.
Thank you.
your code in main.py line189, the code of getting data is below, why do you use companies' stock price to predict NASDAQ-100 Index?
NDX should be calculated by these stock prices, isn’t it? why u have to learn the calculation formula by RNN? The DA-RNN paper gives a time series predicting model, right? But where is your time series predicting? I am confusion.
That's what I found when I read the code repeatedly, If I got wrong or missed something, please tell me. Thank you.