Prophet and NeuralProphet compared with Indian inflation data

The word inflation is a commonly used expression to describe the overall increase in the costs of products and services in the market over time. In this research work, we will look at inflation as a whole, as well as for various population categories and goods within each population group. We will investigate demographic groups such as urban, rural, and combined, and we will compute the real Consumer Price Index (CPI) using the Reserve Bank of India's (RBI) weighted average technique. This research used statistics from 2011 to 2022. The RBI calculates the real CPI using a weighted average technique that takes into consideration the relative significance of various commodities within a population category. This technique is used to guarantee that the CPI accurately reflects the actual expense of living for the population group under consideration. It is essential to observe, that inflation varies significantly between various population groups and commodities. Because of higher living expenses and a higher demand for goods and services, urban areas generally have higher inflation rates. Rural regions, on the other hand have lower inflation rates because of reduced living expenses and lower demand for products and services. We can obtain a clearer grasp of how inflation impacts various segments of the population by analyzing data for these distinct population groups.On the basis of inflation statistics, this article compares two time-series forecasting models, Prophet and NeuralProphet. Both algorithms will be tested on the same data and their accuracy in forecasting will be contrasted. The root mean squared error (RMSE) and Mean absolute error (MAE) will be used as comparative statistics to assess accuracy. The performance of Prophet and NeuralProphet is evaluated in time-series forecasting and to look into the possible benefits of using parallelism on Prophet. This study's results will cast light on the relative strengths of these techniques.