In this example, I am forecasting the future price of the AAPL stock, using data from the kaggle dataset starting with prices after 2015. View complete notebook here.

This model uses the Prophet library

```
from prophet import Prophet
```

After importing the AAPL price data from csv, I created a graph of the price over time.

The dataset includes column for Volume each day, which I will use as a regressor in the prophet model

I trained it on the whole dataset, predicting for future data 31 days in the future.

›

```
# building the model
m = Prophet(
seasonality_mode=sm,
seasonality_prior_scale=sps,
changepoint_prior_scale=cps)
m.add_regressor('Volume')
m.fit(training)
```

Creating predictions is pretty simple, just passing the future data frame.

```
# Forecasting
forecast = m.predict(future)
forecast.head(5)
```

Plotting the predictions

```
# Forecasting
forecast = m.predict(future_df)
m.plot(forecast);
```

The model shows a downward trend for the next 31 days.

It also makes plotting the model components easy. So you can see the yearly, weekly trends

```
# Components
m.plot_components(forecast);
```

I will be forecasting this same dataset using some other forecasting models to compare results. And combining them together.