updated stock price prediction tutorial

Author: x4nth055

machine-learning/stock-prediction/csv-results/2020-12-11_AMZN-sh-1-sc-1-sbd-0-huber_loss-adam-LSTM-seq-50-step-15-layers-2-units-256.csv

@@ -2,12 +2,20 @@
 import time
 from tensorflow.keras.layers import LSTM
 
-
 # Window size or the sequence length
-N_STEPS = 70
+N_STEPS = 50
 # Lookup step, 1 is the next day
-LOOKUP_STEP = 1
-
+LOOKUP_STEP = 15
+
+# whether to scale feature columns & output price as well
+SCALE = True
+scale_str = f"sc-{int(SCALE)}"
+# whether to shuffle the dataset
+SHUFFLE = True
+shuffle_str = f"sh-{int(SHUFFLE)}"
+# whether to split the training/testing set by date
+SPLIT_BY_DATE = False
+split_by_date_str = f"sbd-{int(SPLIT_BY_DATE)}"
 # test ratio size, 0.2 is 20%
 TEST_SIZE = 0.2
 # features to use
@@ -17,7 +25,7 @@
 
 ### model parameters
 
-N_LAYERS = 3
+N_LAYERS = 2
 # LSTM cell
 CELL = LSTM
 # 256 LSTM neurons
@@ -35,12 +43,13 @@
 LOSS = "huber_loss"
 OPTIMIZER = "adam"
 BATCH_SIZE = 64
-EPOCHS = 400
+EPOCHS = 500
 
-# Tesla stock market
-ticker = "TSLA"
+# Amazon stock market
+ticker = "AMZN"
 ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv")
 # model name to save, making it as unique as possible based on parameters
-model_name = f"{date_now}_{ticker}-{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"
+model_name = f"{date_now}_{ticker}-{shuffle_str}-{scale_str}-{split_by_date_str}-\
+{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"
 if BIDIRECTIONAL:
     model_name += "-b"