What is seasonal ARIMA model How to use it?

What is seasonal ARIMA model How to use it?

What is seasonal ARIMA model How to use it?

This recipe explains what is seasonal ARIMA model is and helps you use it


Recipe Objective

A seasonal ARIMA model is formed by including additional seasonal terms in the ARIMA models. The additional seasonal terms are simply multiplied by the non-seasonal terms.

So this recipe is a short example on What is seasonal ARIMA model and how to use it. Let's get started.

Step 1 - Import the library

import numpy as np import pandas as pd from statsmodels.tsa.seasonal import seasonal_decompose import matplotlib.pyplot as plt

Let's pause and look at these imports. Numpy and pandas are general ones. Here, seasonal_decompose will help us in understanding the seasonilty of data.

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.

Now our dataset is ready.

Step 3 - Plotting the pattern

decomposition = seasonal_decompose(df.value, freq=12) fig = plt.figure() fig = decomposition.plot() fig.set_size_inches(20, 10)

Here, we have broken our datset using seasonal_decompose into trend, residual and seasonal. Residual is the stationary pattern and will almost remain constant. Seasonal is what our seasonal pattern will be. Trend is just our upward or downward movement with time.

Step 4 - Creating Seasonal Dataframe

trend = decomposition.trend seasonal = decomposition.seasonal residual = decomposition.resid print(seasonal)

Apart from visualizing our pattern, we have produced various dataframes based on their patter. Finally, we have printed down our seasonal dataframe.

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.

Seasonal, Residual and Trend is visible. Also, we have produced our seasonal dataset in one separte dataframe for further analysis if needed.

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