181 through Multivariate moment in time series forecasting taking advantage of LSTM

For a dataset just search online for ‚yahoo finance GE‘ or any other stock of your interest. Then select history and download csv for the dates you are interested.

Code generated in the video can be downloaded from here:

13 Antworten auf „181 through Multivariate moment in time series forecasting taking advantage of LSTM“

  1. Thanks for your great explanation! Could you tell me how one can train an RNN LSTM model using X1, X2,…(excluding Y) as input and Y as output for a period, and then use this model to predict Y for periot T, using X1, X2, … of the period T?

  2. Thanks for your great explanation Sir! In this example, you forecast open feature. If I want to forecast open and close* (two variables), what are all the changes I have to make ?

  3. Hi Sir, What approach we should follow when the target variable is following sigmoid or logistic or S curve with respect to time.
    Shall we still apply Time Series? If we can which algorithm we should chose as it has multiple variables affecting target variable?

  4. Hi, great tutorial!
    I did not understand the forecast part, maybe you could clarify:
    forecast_period_dates = pd.date_range(list(train_dates)[-1], periods=n_future, freq='1d').tolist()

    forecast = model.predict(trainX[-n_future:]) #forecast

    As I understand this takes : model.predict(trainX[-90:]) or last 90 entries in train_x and makes prediction for next one day as it was trained.
    I dont understand how can it be 90days to future with new dates and not just 89days of train_y and one day predicted.
    Also I have seen in other tutorials it usually : loop: predict next day, append train_x, predict next
    Is this some kind of keras feature?

  5. Sir , let's suppose while predicting a pendullum path, can we just use our time as input variable to predict the bob (x, y) coordinate, something like multivariate output?

  6. ModuleNotFoundError Traceback (most recent call last)

    <ipython-input-68-3279f284c87b> in <module>()


    52 model = Sequential()

    —> 53 model.add(LSTM(64, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))

    54 model.add(LSTM(32, activation='relu', return_sequences=False))

    55 model.add(Dropout(0.2))

    14 frames

    /usr/lib/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)

    ModuleNotFoundError: No module named 'tensorflow.python.ops.while_v2'

  7. Very nice explanation Sir, I always get confused with shape of the data which you explained very well. I had one question, when we predict future values, lets suppose daily prediction then the value of future day 1 should be one input for future day 2 and so on. Please let me know if I am wrong. If my thinking is correct then how this prediction is working. If possible please explain.

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