Time Series Forecasting with ARIMA and SARIMAX Models in Python
In this project, we will be working on time series forecasting, which is a powerful way to understand and predict the trend over time. It will mainly deal with real-time data from industries like Healthcare, Banking, Telecom, and many others. With the help of ARIMA, ARIMAX, and SARIMAX, we will try to identify some patterns in data, test models, and give forecasts based on such models. Are you intimidated by the words ACF plots, stationarity, or residuals? Don't worry, we'll keep it fun and simple for you. In the end, it will all come together for you in one nice little package to show such models could be made to predict future time.
Project Outcomes
Requirements:
Project Description
This project starts by importing and preparing the data. We clean the data, handle missing values, and set the date column as an index. This is how we establish it as a time series. We also set the frequency to monthly data using .asfreq('M'). Next, we explore the data visually. For example, we plot the trends for features like Healthcare, Banking, Telecom, and others. We also generate random white noise to understand the randomness and how it compares with actual data patterns.
The real fun starts when testing the stationarity of the series with the ADF Test. This enables us to make decisions on whether the series requires stationarity before modeling. Then we adopt three different approaches from Arima, Arimax and Sarimax. Finally, we shall compile all the models into a table to find out which has the best performance based on metrics like AIC and Log Likelihood. At the end of this, we will have created a solid forecasting model that is useful in predicting the future of industries like Healthcare.
Build and assess time series forecasting models such as ARIMA, ARIMAX, and SARIMAX using real-world data from sectors like Healthcare and Banking for precise predictions.