Time Series Analysis and Forecasting

Comprehensive Time Series Analysis and Forecasting Project

For this final assignment, you will choose a real-world time series dataset of your choice (e.g., historical stock prices, monthly climate data, or hourly electricity demand). Your task is to perform a complete end-to-end analysis. This includes: 1) Exploratory Data Analysis (EDA) to identify patterns, outliers, and missing values; 2) Data preprocessing and transformations; 3) Testing for stationarity using the Augmented Dickey-Fuller (ADF) test; 4) Decomposing the series into trend, seasonal, and residual components; 5) Building and comparing at least two different forecasting models, such as ARIMA/SARIMA and Exponential Smoothing or Prophet; and 6) Evaluating the performance using metrics like MAE and RMSE, followed by a 12-period-ahead forecast with confidence intervals.