An ARMA or autoregressive moving average model is a forecasting model that predicts future values based on past values. Forecasting is a critical task for several business objectives, such as predictive analytics, predictive maintenance, product planning, budgeting, etc. A big advantage of ARMA models is that they are relatively simple. They only require a small dataset to make a prediction, they are highly accurate for short forecasts, and they work on data without a trend.
In this tutorial, we’ll learn how to use the Python statsmodels package to forecast data using an ARMA model and InfluxDB, the open source time series database. The tutorial will outline how to use the InfluxDB Python client library to query data from InfluxDB and convert the data to a Pandas DataFrame to make working with the time series data easier. Then we’ll make our forecast.
