About me

About us: Embracing the Journey of A.Alim's blog

About Me

Hey there! 👋

Welcome to StatSphere — I’m so glad you stopped by.

I'm Abdul Alim, a data nerd at heart with an MSc in Statistics and a proud GATE qualifier. I created this blog to share my love for statistics, data science, and all things analytics — in a way that’s approachable, practical, and actually useful.

Like many others, I’ve had my fair share of struggles figuring out topics like time series forecasting, ARIMA modeling, ETS, and R programming. I often found that the resources online were either too basic or way too complex. That’s when I decided:

Why not build a space that bridges that gap?

So, StatSphere was born — a platform where I simplify complex concepts, share hands-on tutorials, and explore the intersection of statistics, business, and programming.

Here, you'll find:

Step-by-step guides on R and time series modeling
Statistical explanations with real-world relevance
Data science insights that connect theory to practice
And a bit of the journey that got me here

Whether you're a student, a data science enthusiast, or a curious mind trying to make sense of the data world — you're welcome here.

Let’s keep learning, experimenting, and growing together.

Thanks for being here — I truly appreciate it 🙌

Cheers,

Abdul Alim

Comments

Time Series Forecasting in R with Facebook Prophet – A Beginner’s Guide

📦 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...

Time Series Analysis: Unveiling Trends and Forecasting the Future

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 + ...

Causal Discovery in Time Series: Untangling Time, Correlation & Causation

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...

StatSphere

Search This Blog