https://github.com/msikorski93/forecasting-inflation-rates-of-poland

Recently inflation is a popular topic in Poland and is highest since 2001. Experts presume inflation in Poland should continue to rise, and by the end of 2021 it will be close to 8%. This notebook aims to develop a forecasting model for time series using Python.
https://github.com/msikorski93/forecasting-inflation-rates-of-poland

autoregressive-models cnn inflation-forecasting lstm sarima statsmodels tensorflow time-series-forecasting

Last synced: 6 months ago
JSON representation

Recently inflation is a popular topic in Poland and is highest since 2001. Experts presume inflation in Poland should continue to rise, and by the end of 2021 it will be close to 8%. This notebook aims to develop a forecasting model for time series using Python.

• Host: GitHub
• URL: https://github.com/msikorski93/forecasting-inflation-rates-of-poland
• Owner: msikorski93
• Created: 2021-12-07T00:00:14.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-03-11T15:02:52.000Z (over 1 year ago)
• Last Synced: 2025-04-05T01:23:43.226Z (7 months ago)
• Topics: autoregressive-models, cnn, inflation-forecasting, lstm, sarima, statsmodels, tensorflow, time-series-forecasting
• Language: Jupyter Notebook
• Homepage:
• Size: 2.89 MB
• Stars: 16
• Watchers: 2
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Forecasting-Inflation-Rates-of-Poland

Recently inflation is a popular topic in Poland and is highest since the 90s. Experts presume inflation in Poland should continue to rise. The time series forecasting was completed by implementing the autoregressive methods.

* New update for March 2024. Inflation values were forecasted with deep learning: CNN & LSTM. The out-of sample predictions confirm that Polish inflation will continue to fall. Based on evaluation metrics and model residuals, the CNN is a better choice.

|          | RMSE   | R2 | MAE    | MAPE [%] |

|----------|--------|---------|--------|----------|

| **CNN**  | 0.4008 | 0.9892  | 0.3012 | 0.1753   |

| **LSTM** | 0.4957 | 0.9835  | 0.3736 | 0.2218   |

* New update for March 2023.

![march_2023](https://user-images.githubusercontent.com/45270023/225881806-0ef77db7-9e9a-426d-88c2-0291da383201.jpg)

* The November 2022 is an updated and much fresher version of the forecast. It contains a SARIMA implementation.

![forecast](https://user-images.githubusercontent.com/45270023/202861900-a2e3db91-a6bf-4524-940f-1c95748eacde.jpg)

* The earlier notebook (December 2021) describes the ARIMA method (without seasonality order). The notebook contains factual errors both in code and description.