How can I predict the following value of time series using the Tensorflow predict method?
Thank you for reading. I’m not good at English.
I am wondering how to predict and get future time series data after model training.
I wonder if the time series data has been properly learned and predicted.
How can I do this right to get the following(next) value?
x_test[-1] == t So, the meaning of the next value is
t+1, t+2, .... t+n,
in this example I want to get
t+1, t+2 ... t+n
I tried using stock index data
inputs = total_data[len(total_data) - forecast - look_back:] inputs = scaler.transform(inputs) X_test =  for i in range(look_back, inputs.shape): X_test.append(inputs[i - look_back:i]) X_test = np.array(X_test) predicted = model.predict(X_test)
but the result is like below
The results from
X_test[-20:] and the following 20 predictions looks like same.
I’m wondering if it’s the correct train and predicted value.
and I’m wondering if it was the right training and predict.
The method I tried first did not work correctly.
I realized something is wrong, I tried using another official data
So, I used the time series in the Tensorflow tutorial to practice predicting the model.
a = y_val[-look_back:] for i in range(N-step prediction): #predict a new value n times. tmp = model.predict(a.reshape(-1, look_back, num_feature)) #predicted value a = a[1:] #remove first a = np.append(a, tmp) #insert predicted value
The results were predicted in a linear regression shape very differently from the real data.
Output a linear regression abnormal that is independent of the real data:
full source (After the 25th line is my code.)
I’m really very curious that How can I predict the following value of time series using Tensorflow predict method
I’m not wondering if this works or not working with a theoretically. I’m just wondering how to get the following n steps using the predict method.
Thank you for reading the long question. I seek advice about your priceless opinion.