What does it mean for a timeseries to have multiple seasonalities?
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# What does it mean for a timeseries to have multiple seasonalities?

This recipe explains what does it mean for a timeseries to have multiple seasonalities

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## Recipe Objective

Consider sales data contains daily observations. It exhibits weekly and yearly seasonal patterns. It means we are dealing with time series containing multiple seasonal effects. Now this time series has multiple seasonities effects. It can best be cared by TBATS or BATS.

So this recipe is a short example on does a time series have multiple seasonalities? What does it mean?. Let's get started.

## Step 1 - Import the library

``` import numpy as np import pandas as pd from tbats import BATS,TBATS ```

Let's pause and look at these imports. Numpy and pandas are general ones. bats will help us in building the model; make sure you have preinstalled it in your system.

## Step 2 - Setup the Data

``` df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date']).set_index('date') ```

Here, we have used one time series data from github. Also, we have set our index to date.

## Step 3 - Splitting dataset

``` train_data = df[1:len(df)-12] test_data = df[len(df)-12:] ```

We have split our dataset into train and test samples.

## Step 4 - Creating TBAT, BATS & predicting

``` estimator_tbats = TBATS(seasonal_periods=(7, 365.25)) model_tbats = estimator.fit(train_data) estimator_bats = BATS(seasonal_periods=(7, 365.25)) model_bats = estimator.fit(train_data) y_forecast_tbats = model_tbats.forecast(steps=365) y_forecast_bats = model_bats.forecast(steps=365) print(y_forecast_tbats) print(y_forecast_bats) ```

Simply, using TBATS/BATS library, we have created an object of TBATS/BATS class and thereby fit our datset on the train. Finally we are predicting the datset for the next 1 year (365 days).

## Step 5 - Lets look at our dataset now

Once we run the above code snippet, we will see:

```Srcoll down the ipython file to visualize the results.
```

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