StatSphere

Summary Box Statistics powers daily tools from weather apps to credit decisions. R makes it easy to visualize and analyze real-world data. Seemingly simple percentages are often backed by deep statistical models. You can explore your own local patterns using public data.  Call to action: Next time you see a percentage or average, ask: What data was included (and excluded)? How was it collected? Who benefits from this interpretation? Introduction: The Invisible Force Around You 1.1: Hook the reader Did your weather app say "70% chance of rain" today? That percentage didn't come from magic—it's statistics at work. From the moment you check your phone in the morning to when you scroll Netflix at night, statistics quietly shapes nearly every decision you make. 1.2: Define statistics briefly Statistics isn't just boring numbers—it's the science of finding meaning in data. Whether it's calculating the average rating of your favorite coffee sh...

📦 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...

  Introduction to Time Series Analysis What is Time Series Analysis? Time series analysis is a statistical method used to analyze time-ordered data points collected at regular intervals. It helps identify patterns, trends, and seasonal effects in data, making it a crucial tool for forecasting and decision-making across various industries. Key Components of Time Series Understanding time series requires breaking it down into its fundamental components: Trend: The long-term movement of data, either upward or downward. Example: Global temperature rise over decades. Seasonality: Regular fluctuations at specific intervals, such as increased retail sales during the holiday season. Cyclicality: Recurrent patterns occurring over irregular periods, such as economic cycles. Irregularity (Noise): Random fluctuations that do not follow a pattern, like stock market crashes. Mathematically, a time series can be represented as: Y t = T t + S t + C t + I t Y_t = T_t + S_t + C_t + ...

Key Points Time series analysis is used to predict future events based on historical data. Key components of time series include trend, seasonality, cyclicality, and irregularity. Common forecasting methods: ARIMA, SARIMA, Exponential Smoothing, and Machine Learning (RNNs, LSTMs). Applications in finance, retail, meteorology, healthcare, and energy. Emerging trends: Deep learning, probabilistic forecasting, and big data analytics. Model evaluation metrics: MAE, MSE, RMSE, MAPE. Time series analysis is also crucial for climate change research and mitigation strategies. Introduction Time series analysis is a powerful statistical method used to predict future events based on past data collected over time. Whether it's forecasting stock prices, predicting weather patterns, or estimating energy consumption, time series analysis plays a crucial role in decision-making across industries. This post will explore the fundamental concepts, key forecasting methods, real-world applicati...