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.

Data source: https://fred.stlouisfed.org/

Modelos de alta dimensionalidade para previsão do IPCA

LSTM neural network for inflation forecasting

This repository contains the code for a research on inflation prediction using different Machine Learning methods. It was developed by @estcab00 for @qlabpucp

Data Analytics on inflation data set using machine learning algorithms

Inflation-forecasting pipeline that combines ARIMA diagnostics in Stata with LSTM tuning in Python. It covers CPI data preparation, exploratory analysis, model selection, residual checks, and dynamic multi-step forecasts, then benchmarks econometric and deep-learning approaches with MSE, MAE, and R^2 plus clear visuals.

Group 12's 2024/25 academic year COMP5530M group project on inflation forecasting with machine learning models.

A different way to hedge against inflation.

Inflation forecasting during crisis periods using Bayesian Dynamic Linear Models, traditional econometrics, and machine learning. Includes data, code, and comprehensive analysis report.

To address the impact of rising house prices on the economy, we built a machine learning model resistant to market trends. We experimented with Random Forest and Linear Regression models, employing sophisticated imputation methods like median state price replacement, KNN imputation, and forward/backward filling to minimize errors.

A Canada Inflation Prediction project carried out by deploying LSTM.

Calculations for my data-managment final project. Inflation prediction

Modeling and forecasting inflation in Sri Lanka using ARIMA models

Inflation Forecasting

In this project, I extracted +40 leading indicators to forecast the UK inflation rate from Twitter tweets from 2018 to 2022 in the UK for the Data-Driven Economics course in my master's at Sapienza University

"Impact of International Shocks on North Macedonia and the Western Balkans"

PPTI 15 Final Project for Machine Learning Classes.

A modified Philips Curve to estimate U.S. inflation at risk using R programming. The data is imported from FRED using API.

No description provided.

This project was completed as the final assignment for the Fixed Income Securities Analysis and Modeling course at Zhejiang University. It employs the XGBoost algorithm to forecast future inflation rates using forward interest rates as key predictors.

I prepared a modified version of the Taylor Rule to measure inflation at risk for the U.S. economy using R programming.