Time Series Forecasting

Introduction to Time Series Forecasting

Time series forecasting models are used to predict future values based on past data. These models are crucial in various fields such as finance, weather forecasting, and more.

Context and Importance

Time series forecasting is important because it enables organizations to make informed decisions about future outcomes. For instance, in finance, time series forecasting can be used to predict stock prices, while in weather forecasting, it can be used to predict temperature and precipitation.

Core Concept

The core concept of time series forecasting models is to identify patterns in the data and use these patterns to make predictions about future values. There are several types of time series forecasting models, including autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models.

Autoregressive (AR) Models

Autoregressive models are based on the idea that the current value of a time series is a function of past values. The general form of an AR model is:

import numpy as np

def ar_model(data, p):