I have a problem I don't know how to fix transform to add new features in order to make more proper forecast. The code below predicts stock prices by Close value. Data:
Open High Low Close Adj Close Volume
Datetime
2020-03-10 09:30:00+03:00 5033.0 5033.0 4690.0 4840.0 4840.0 702508
2020-03-10 10:30:00+03:00 4840.0 4870.0 4700.0 4746.5 4746.5 1300648
2020-03-10 11:30:00+03:00 4746.5 4783.0 4706.0 4745.5 4745.5 1156482
2020-03-10 12:30:00+03:00 4745.5 4884.0 4730.0 4870.0 4870.0 1213268
2020-03-10 13:30:00+03:00 4874.0 4990.5 4867.5 4886.5 4886.5 1958028
... ... ... ... ... ... ...
2020-04-03 14:30:00+03:00 5177.0 5217.0 5164.0 5211.5 5211.5 385696
2020-04-03 15:30:00+03:00 5212.0 5364.0 5191.0 5269.5 5269.5 1091066
2020-04-03 16:30:00+03:00 5270.0 5297.0 5209.0 5220.5 5220.5 518686
2020-04-03 17:30:00+03:00 5222.0 5271.0 5184.0 5220.5 5220.5 665096
2020-04-03 18:30:00+03:00 5217.5 5223.5 5197.0 5204.5 5204.5 261400
I want to add Volume and Open features, but getting error:
predictions = scaler.inverse_transform(predictions)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/preprocessing/_data.py", line 436, in inverse_transform
X -= self.min_
ValueError: non-broadcastable output operand with shape (40,1) doesn't match the broadcast shape (40,3)
Q1: How to change inverse_transform and what else do I need to change (input_shape argument maybe) to get correct results?
Q2: The result will be prediction of Close value. But how do I predict Volume value also? I guess I need to set model.add(Dense(2)), but can I do 2 predictions correctly in one code, or I need to execute script separately? How to do that? How do I get Volume than Open when model.add(Dense(2))?
完整代码：
from math import sqrt
from numpy import concatenate
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding
from keras.layers import LSTM
import numpy as np
from datetime import datetime, timedelta
import yfinance as yf
start = (datetime.now() - timedelta(days=30))
end = (datetime.now() - timedelta(days=0))
df = yf.download(tickers="LKOH.ME", start=start.strftime("%Y-%m-%d"), end=end.strftime("%Y-%m-%d"), interval="60m")
df = df.loc[start.strftime("%Y-%m-%d"):end.strftime("%Y-%m-%d")]
# I need here add another features
# df.filter(['Close', 'Open', 'Volume']) <-- this will make further an error with shapes
data = df.filter(['Close'])
dataset = data.values
#Get the number of rows to train the model on, 40 rows for test
training_data_len = len(dataset) - 40
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
train_data = scaled_data[0:int(training_data_len), :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i-60:i, 0])
y_train.append(train_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
# should i change to input_shape=(x_train.shape[1], 3) ?
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, batch_size=1, epochs=1)
test_data = scaled_data[training_data_len - 60: , :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
x_test.append(test_data[i-60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1 ))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions) # error here
