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This text is a follow-up to a earlier submit. There, we recognized 3 kinds of seasonal patterns.

Right here, we’ll:

- Discover ways to describe the seasonality of a time sequence.
- Go over 8 approaches you should utilize to mannequin seasonality.

Seasonality refers to repeatable patterns that recur over some interval. It is a vital supply of variation that’s essential to mannequin.

There are a number of methods of dealing with seasonality. Some approaches take away the seasonal part earlier than modeling. Seasonally-adjusted knowledge (a time sequence minus the seasonal part) highlights long-term results resembling tendencies or enterprise cycles. Different approaches add additional variables that seize the cyclical nature of seasonality.

Earlier than going over totally different strategies, let’s create a time sequence and describe its seasonal patterns.

## Evaluation instance

We’ll use the identical course of we did within the earlier article (see additionally reference [1]):

`interval = 12 # month-to-month sequence`

measurement = 120beta1 = np.linspace(-.6, .3, num=measurement)

beta2 = np.linspace(.6, -.3, num=measurement)

sin1 = np.asarray([np.sin(2 * np.pi * i / 12) for i in np.arange(1, size + 1)])

cos1 = np.asarray([np.cos(2 * np.pi * i / 12) for i in np.arange(1, size + 1)])

xt = np.cumsum(np.random.regular(scale=0.1, measurement=measurement))

yt = xt + beta1 * sin1 + beta2 * cos1 + np.random.regular(scale=0.1, measurement=measurement)

yt = pd.Sequence(yt)

Right here’s what this sequence appear like:

We are able to begin by describing the seasonal sample by its power:

`# https://github.com/vcerqueira/weblog/tree/major/src`

from src.seasonality import seasonal_strengthseasonal_strength(yt, interval=12)

# 0.90

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