Getting a probability distribution curve from a TensorFlow model-CodePudding

I am trying to learn working with TensorFlow and so I was trying to make a probablistic ML model to get the probability distribution of the next day stock price based on the last n days price sequence, and when doing so I managed to predict the next day's price but not getting a probability distribution of the model. How do I get the curve which the model predictions are based on from the TensorFlow model?

This is the code i have got so far that predicts the actual price for the next day (using this video: https://www.youtube.com/watch?v=PuZY9q-aKLw):

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

from datetime import datetime
from datetime import timedelta

from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfpl = tfp.layers
tfk = tf.keras
tfkl = tf.keras.layers

# preparing data
min_max_scaler = MinMaxScaler(feature_range=(0,1)) # a scaler object which normalizes the number between 0 and 1 in relation to the rest of the dataset
close_prices = price_data.loc[:,'Close'].values # all the close prices of the price_data df
scaled_close_prices = min_max_scaler.fit_transform(close_prices.reshape(-1,1)) # all the close prices, normalized between 1 and 0

n = 60 # the number of days that are used to determine the next value

# x contains the last n days before the predicted day which is y
X_train = [] # 70% of the dataset
y_train = []
X_test = [] # 30% of the dataset
y_test = []

for x in range(n, int(len(scaled_close_prices)*0.7)):
    X_train.append(scaled_close_prices[x-n:x,0])
    y_train.append(scaled_close_prices[x,0])

for x in range(int(len(scaled_close_prices)*0.7), len(scaled_close_prices)):
    X_test.append(scaled_close_prices[x-n:x,0])
    y_test.append(scaled_close_prices[x,0])

X_train, y_train = np.array(X_train), np.array(y_train)
X_test, y_test = np.array(X_test), np.array(y_test)

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# building the model
model = tfk.Sequential()

model.add(tfkl.LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(tfkl.Dropout(0.2))
model.add(tfkl.LSTM(units=50, return_sequences=True))
model.add(tfkl.Dropout(0.2))
model.add(tfkl.LSTM(units=50))
model.add(tfkl.Dropout(0.2))
model.add(tfkl.Dense(units=1))

# compiling model
model.compile(optimizer=tfk.optimizers.Adam(learning_rate=0.05),
              loss='mean_squared_error',
              metrics=[])
# fitting model
model.fit(X_train, y_train, epochs=25, batch_size=32)

# testing model
y_predicted = model.predict(X_test)
y_predicted = min_max_scaler.inverse_transform(y_predicted).reshape(-1)

CodePudding user response：

Probabilistic modelling one of the things that tensorflow_probability (tfp) can do.

It can do probabilistic time series forecasting - e.g. see https://www.tensorflow.org/probability/examples/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand

... but I don't believe it includes a variational LSTM in its toolbox, and they may be close to the current research frontier. e.g. see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842932/

CodePudding user response：

As written, this model is not especially probabilistic, per se, but it can be viewed as outputting an estimate of the mean of a gaussian distribution with unit variance; this is implicit in using a squared error loss. To make this more concrete, you could instantiate a tfd.Normal distribution with the output of your model as the loc parameter and scale=1., and call prob(...) to evaluate the pdf of this distribution. I'm not sure if this is what you're looking for, though.