ARIMA-Based Trend and Seasonality Analysis
A multi-dataset forecasting project using decomposition, stationarity testing, and ARIMA modeling to uncover long-term behavior in production, population, and commodity price series.
Primary audience
Screenshots coming soon
Story
The problem
Decision-making needed clearer signals on trend direction, seasonality, and volatility across multiple domains.
The approach
Applied descriptive analytics, ADF tests, and auto.arima models in R to compare behavior across three separate datasets.
The outcome
The analysis highlighted declining and rising long-run trends, seasonal effects, and confidence-bound uncertainty for forward planning.
Overview
This project analyzes monthly time series datasets from 2005 to 2025, including airplane production, population, and rice prices. The workflow combines exploratory statistics, decomposition into trend/seasonal/residual components, Augmented Dickey-Fuller stationarity checks, and ARIMA forecasting for the next 12 months. Built in R with ggplot2, forecast, and tseries, it provides a structured decision-support pipeline for understanding long-horizon dynamics and uncertainty in real-world data.
Capabilities
Computed central tendency, dispersion, and histograms to establish baseline behavior and variability for each dataset.
Split each series into trend, seasonal, and residual components to separate long-run movement from cyclical effects.
Applied Augmented Dickey-Fuller testing to identify non-stationarity and guide model assumptions before forecasting.
Used auto.arima with monthly frequency settings and generated forward predictions with uncertainty intervals.
Compared behavior across airplane production, population trends, and rice prices to identify dataset-specific dynamics.
Documented a clear end-to-end code path for loading, cleaning, testing, modeling, plotting, and reporting.
Engineering
Results
Delivered a reusable monthly time-series analytics and forecasting workflow
Identified long-term trends, seasonal behavior, and volatility across multiple domains
Validated non-stationarity and modeled short-term future movement with ARIMA
Produced interpretable visual outputs for stakeholder decision-making
Established a solid foundation for deeper econometric and causal analysis