Explain MA modelling of time series?
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Explain MA modelling of time series?

Explain MA modelling of time series?

This recipe explains MA modelling of time series

Recipe Objective

The moving average (MA) method models the next step in the sequence as a linear function of the residual errors from a mean process at prior time steps. A moving average model is different from calculating the moving average of the time series.

So this recipe is a short example on what is MR modelling of time series. Let's get started.

Step 1 - Import the library

import numpy as np import pandas as pd from statsmodels.tsa.arima_model import ARMA

Let's pause and look at these imports. Numpy and pandas are general ones. Here statsmodels.tsa.arima_model is used to import ARMA library for building of model.

Step 2 - Setup the Data

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

Here, we have used one time series data from github.

Now our dataset is ready.

Step 3 - Splitting Data

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

Here we have simply split data into size of 12 and rest elements

Step 4 - Building MR model

model = ARMA(train_data.value, order=(0, 1)) model_fitted = model.fit()

We can use the ARMA class to create an MA model and setting a zeroth-order AR model. We must specify the order of the MA model in the order argument.

Step 5 - Printing the results

print('coefficients',model_fitted.params) predictions = model_fitted.predict(start=len(train_data), end=len(train_data) + len(test_data)-1) print(predictions)

Here, we have printed the coeffiecient of model and the predicted values.

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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