How to decompose a time series?
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How to decompose a time series?

How to decompose a time series?

This recipe helps you decompose a time series

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Recipe Objective

Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and noise components.

So this recipe is a short example on how to decompose a time series. Let's get started.

Step 1 - Import the library

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

Let's pause and look at these imports. Numpy and pandas are general ones. Here matplotlib.pyplot will help us in plotting. statsmodels.tsa.seasonal comes handy while analysing patterns.

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 - Understanding Trends

result = seasonal_decompose(df, model='additive')

We have simply created an object of seasonal_decompose to understand our model with additive feature.

Step 4 - Printing results

print(result.trend) print(result.seasonal) print(result.resid) print(result.observed)

We are priting the decomposition of our dataset in here

Step 5 - Visualizing

result.plot() plt.show()

Finally, we have tried to visualize trends in one go.

Step 6 - Lets look at our dataset now

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

Scroll down the ipython file to visualize the output.

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