A FORECASTING STUDY ON CONSUMER PRICE ANALYSIS USING POWER BI

Abstract

Consumer price inflation is one of the most consequential macroeconomic indicators, directly shaping purchasing power, monetary policy, and household welfare across economies. This paper presents a forecasting study on Consumer Price Index (CPI) analysis using Microsoft Power BI as the primary visualization and analytical platform. The study integrates seven years of monthly CPI data across eight commodity groups in India (FY 2017–18 to FY 2023–24), sourced from the Ministry of Statistics and Programme Implementation (MoSPI) and the Reserve Bank of India (RBI). Time series analysis techniques including Moving Average, Exponential Smoothing, and ARIMA-derived trend modeling are applied within Power BI’s forecasting engine and supplemented by Pythonscripted predictive models. Interactive Power BI dashboards present CPI decomposition, category-wise inflation trends, urban versus rural price differentials, and a twelve-month forward forecast. The study identifies Food & Beverages and Fuel & Light as the two highest-volatility CPI components, demonstrates the widening urban–rural inflation gap, and provides statistically validated twelve-month price forecasts with 95% confidence intervals. Findings carry direct implications for monetary policymakers, retail enterprises, and household financial planners.