Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model
In this post I show you how to predict stock prices using a forecasting LSTM model
1. Introduction
1.1. Time-series & forecasting models
Traditionally most machine learning (ML) models use as input features some observations (samples / examples) but there is no time dimension in the data.
Time-series forecasting models are the models that are capable to predict future values based on previously observed values. Time-series forecasting is widely used for non-stationary data. Non-stationary data are called the data whose statistical properties e.g. the mean and standard deviation are not constant over time but instead, these metrics vary over time.
These non-stationary input data (used as input to these models) are usually called time-series. Some examples of time-series include the temperature values over time, stock price over time, price of a house over time etc. So, the input is a signal (time-series) that is defined by observations taken sequentially in time.
A time series is a sequence of observations taken sequentially in time.
