import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, LSTM from sklearn.preprocessing import MinMaxScaler import numpy as np # Load and preprocess data # … # Build the neural network model = Sequential() 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)) # Compile the model model.compile(optimizer=”adam”, loss=”mean_squared_error”) # Train the model
Minutes from the Reserve Bank of New Zealand meeting: Committee noted inflation is still expected to decline within the target band by the second half of 2024 Committee agreed that the risks around the inflation projection remain balanced Committee noted that the estimate of the nominal neutral OCR has increased by 25 basis points to
An artificial neural network is made up of several layers of interconnected “neurons”, each layer having a specific role in the data processing process. The three main types of layers are: Input Layer: This layer receives the raw data as input. In the context of trading, this can be historical data such as open prices,