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.

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