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📦 Summary Box Topic: Time Series Forecasting with Facebook Prophet in R Dataset Used: AirPassengers (Monthly Airline Passenger Numbers 1949–1960) Tool: Facebook Prophet Goal: Forecast future values and evaluate prediction accuracy Key Features: Handles trend and seasonality automatically Easy to use with minimal tuning Visual and interpretable outputs Evaluation Metrics: MAE, RMSE, MAPE Best For: Business, Web Traffic, and Seasonal Forecasting Key Points Research suggests time series analysis predicts future trends using historical data, like stock prices or weather patterns. Common methods include ARIMA, SARIMA, exponential smoothing, and machine learning models like RNNs and LSTMs. Prophet automates trend and seasonality modeling and is especially suitable for business and web forecasting scenarios. Forecast evaluation using metrics like MAE, MSE, RMSE, and MAPE is essential to compare model performance. ⚠️ Note: To fix the error do not know how to convert 'ti...

Causal Discovery in Time Series: Untangling Time, Correlation & Causation Introduction "Correlation is not causation" is a mantra every statistician lives by. However, when it comes to time series data, the very structure of time gives us something to work with. After all, if variable A precedes variable B consistently, can we say A causes B? In this post, we dive into one of the most intriguing challenges in time series analysis: discovering causality from observational data. We will explore classic and modern methods for identifying causality, their assumptions, limitations, and real-world applications. By the end, you’ll be equipped with tools and insights to experiment with causal inference in your time series data. What Is Causality in Time Series? Causality goes beyond correlation. It implies a directional influence — a cause must precede its effect. In time series, this temporal aspect offers a foothold to infer causality. However, time ordering alone is not enoug...