In this simple tutorial, we will have a look at applying a time series model to stock prices. More specifically, a non-seasonal ARIMA model. We implement a grid search to select the optimal parameters for the model and forecast the next 12 months. The ARIMA (p,d,q) model The acronym ARIMA stands for Auto-Regressive Integrated Moving Average and is one of the most common tools for forecasting a time series. Before we can apply ARIMA to a time series, the time series needs to be stationary. Stationary implies that the statistical properties are all constant over time, i.e there is no…...
Introduction to Time Series Forecasting of Stock Prices with Python
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