# Time Series Analysis & Forecasting

> ARIMA, Prophet, LSTM, and transformer-based forecasting — from classical statistics to deep learning for temporal data.

![Status: complete](https://img.shields.io/badge/status-complete-brightgreen)

Open [`26-time-series-analysis.ipynb`](./26-time-series-analysis.ipynb) for learning objectives, prerequisites, and recommended reading order.

## Topics Covered

- Time series fundamentals: stationarity, autocorrelation, seasonality
- Classical methods: ARIMA, SARIMA, exponential smoothing
- Facebook Prophet for trend and seasonality modelling
- Deep learning: LSTM, GRU, temporal CNN, and transformer forecasters

## Prerequisites

[02-data-science/README.md](../02-data-science/README.md) · [03-maths/README.md](../03-maths/README.md)

## What Comes Next

[27-causal-inference/README.md](../27-causal-inference/README.md)
