twebberbr/project-time-series-forecast-beer-production

This project aims at forecasting the beer production in megaliters for years 1995-2000 using the historical data

Project-Time-Series-Forecast-Beer-Production

This project aims at forecasting the beer production in megaliters for years 1995-2000 using the historical data

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Australian Beer Production Forecast (1996)

Overview

This project aims to predict the monthly beer production in Australia for the years 1996-2000. While the provided dataset covers several decades from 1956 to 1995, it contains only two columns: Time frame and beer production. It's intuitive to think that multiple factors influence beer production in a country, including temperature, price, advertising, economic and political factors, and more. However, this project primarily focuses on forecasting beer production using the historical data at hand.

The unit of monthly beer production is in megaliters.

Methodology

To achieve the goal of predicting beer production, this project employed the SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors) model. This statistical model is commonly used for time series forecasting, allowing us to account for seasonality and other temporal patterns that might impact beer production.

SARIMAX Model

SARIMAX combines ARIMA (AutoRegressive Integrated Moving Average) with exogenous regressors, allowing us to incorporate external factors into our forecasting model. In a practical sense, this means we can make predictions by considering historical production data along with any additional factors we believe may influence beer production.

Results

The SARIMAX model was trained on the available dataset, and forecasts were generated for the year 1996. The forecast values for monthly beer production in Australia for the years 1995-09 to 2000 are as follows:

          Forecast

1995-09-01 129.201730 1995-10-01 164.717849 1995-11-01 190.498579 1995-12-01 180.611712 1996-01-01 150.036754 1996-02-01 138.649104 1996-03-01 148.419637 1996-04-01 136.291654 1996-05-01 145.038382 1996-06-01 119.225425 1996-07-01 133.319343 1996-08-01 138.687390 1996-09-01 130.435667 1996-10-01 172.889229 1996-11-01 181.990136 1996-12-01 181.786655 1997-01-01 152.244477 1997-02-01 136.617234 1997-03-01 146.481100 1997-04-01 140.697603 1997-05-01 138.117874 1997-06-01 119.088996 1997-07-01 135.822438 1997-08-01 134.390670 1997-09-01 133.560464 1997-10-01 169.509651 1997-11-01 171.332673 1997-12-01 183.894687 1998-01-01 148.647043 1998-02-01 133.846982 1998-03-01 150.079997 1998-04-01 141.105383 1998-05-01 128.636869 1998-06-01 122.169779 1998-07-01 133.270201 1998-08-01 133.876571 1998-09-01 136.885463 1998-10-01 160.129298 1998-11-01 169.495371 1998-12-01 184.650552 1999-01-01 141.350342 1999-02-01 131.949531 1999-03-01 153.578572 1999-04-01 135.177633 1999-05-01 126.943275 1999-06-01 124.457053 1999-07-01 126.253276 1999-08-01 137.524013 1999-09-01 135.166138 1999-10-01 153.843010 1999-11-01 177.158305 1999-12-01 181.901088 2000-01-01 137.474124 2000-02-01 131.871581 2000-03-01 151.912753 2000-04-01 128.544292 2000-05-01 133.499525 2000-06-01 122.142023 2000-07-01 121.629207 2000-08-01 139.873329 2000-09-01 129.259091 2000-10-01 156.462143 2000-11-01 184.398451 2000-12-01 177.605366

Dependencies

pandas numpy seaborn matplotlib scipy statsmodels

How to Run the Code

The .ipynb file in this repository gives the step by step instructions to go ahead with forecasting any similar models.