Concept of ARIMA, and Insights from Data Scientist at BharatPe

Welcome to this edition of the Data Pragmatist, your dose of all things data science and AI. A warm welcome to the 529 new members who joined our community of over 8,000 data professionals since this Monday.

📖 Estimated Reading Time: 4 minutes. Missed our previous editions? Catch up on some insightful reads here:

• Concept of Covariance, LLMs, and Insights from Industry Experts

• The AI Revolution in Higher Education: Balancing Promise and Caution

Today we are talking about ARIMA (Autoregressive Integrated Moving Average) which is a time series forecasting technique that combines past values, differencing, and error terms to make predictions based on historical data patterns. We follow this with our interview series “Analytical Voyages” with Senior Data Scientist at BharatPe. Do follow us on Linkedin and Twitter for more real-time updates.

— Arun Chinnachamy

Top News this week: ChatGPT can now see, hear, and speak

Revolutionizing ChatGPT: Voice and Image Integration

ChatGPT is undergoing a transformation with the addition of voice and image capabilities, making interactions more dynamic:

• Voice Conversations: Engage in two-way conversations, from settling debates to requesting bedtime stories.

• Visual Interaction: Snap images for instant information, recipes, or educational help.

• Practical Use: Apply these features in daily life, enhancing ChatGPT's utility.

• Accessible: Voice and image are rolling out to Plus and Enterprise users on all platforms, offering a richer AI experience.

Unfortunately it is still being rolled out starting with plus users. So we need to wait for sometime before we can try it out.

📚 Must Reads for the week

1. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything by Stephen J. Dubner and Steven Levitt:

   • This book explores everyday beliefs using econometrics and data mining to debunk them, teaching the foundations of data science: asking the right questions, identifying patterns, and challenging conventional wisdom.

2. Algorithms to Live By by Brian Christian and Tom Griffiths:

   • It provides a deep understanding of computer science algorithms and how they apply to real-life decision-making, making it a valuable read for data scientists who need to grasp algorithms and data structures.

3. The Black Swan: The Impact of the Highly Improbable by Nassim Nicholas Taleb:

   • Discusses the extreme impact of rare, unpredictable events (Black Swan Events) and teaches about uncertainty, probability, and risk, which is valuable for data scientists dealing with outliers and global events.

4. Top 5 ChatGPT Courses to Master Natural Language Processing: ChatGPT, an OpenAI language model, empowers various applications from chatbots to content creation. Discover its power with the top 5 ChatGPT courses. Click here to read this insightful article.

🧠Feature: Understanding ARIMA (Auto-regressive Integrated Moving Average) Time Series Analysis

ARIMA (Autoregressive Integrated Moving Average) is a class of statistical models that are used to analyze and forecast time series data. ARIMA models are based on the assumption that the future values of a time series are predictable based on its past values.

ARIMA Model Components

ARIMA models are composed of three main components:

1. Autoregressive (AR) component: The AR component models the relationship between the current value of the time series and its past values.

2. Integrated (I) component: The I component models the non-stationarity of the time series. A time series is non-stationary if its mean or variance changes over time.

3. Moving Average (MA) component: The MA component models the relationship between the current value of the time series and the past errors (residuals) from the model.

ARIMA Model Building Process

• Order Identification: In this step, we decide how many autoregressive (AR), differencing (I), and moving average (MA) terms to include in the model by studying the autocorrelation and partial autocorrelation functions.

• Parameter Estimation: Once the order is set, we calculate the specific values for the AR, I, and MA coefficients using statistical methods like OLS or MLE.

• Forecasting: With the model's parameters in place, we use historical data and the model equation to make predictions about future values of the time series.

ARIMA Model Assumptions

ARIMA models make a number of assumptions about the data, including:

• The time series is stationary.

• The errors (residuals) from the model are normally distributed.

• The errors (residuals) from the model are independent and identically distributed (i.i.d.).

ARIMA Model Applications

ARIMA models can be used to forecast a wide variety of time series data, including:

1. Sales data

2. Stock prices

3. Economic indicators

4. Weather data

5. Consumer confidence

ARIMA models are a powerful tool for analyzing and forecasting time series data. ARIMA models are relatively simple to understand and implement, and they can be used to forecast a wide variety of time series data. However, ARIMA models can be sensitive to the order of the model, and they can be difficult to use to forecast non-stationary time series data.

 🚀 Analytical Voyages #3 - Interview with Anshu📊

We have started a new interview series where we interview data professionals across the globe to bring in real life insights, expert advices and tips for young professionals to grow from wisdom of others.

This week, we spoke with Anshu Trivedi, a Data Scientist at BharatPe, a leading fintech company in India. Here is the summary of our conversation and link to full interview.

Keep Learning: Anshu suggests always being ready to learn new things in the fast-changing data science field. She emphasizes the need to explore new AI concepts and expand your skill set to stay up-to-date.

Explore Broadly: For newcomers, Anshu recommends exploring various areas within data science instead of focusing on one niche early on. She suggests using platforms like Coursera and Udacity to learn the basics.

Soft Skills Matter: Anshu highlights the importance of soft skills like good communication and teamwork. These skills are essential for working well with others and advancing in your career.

Understand the Business: Moving up in data science also requires understanding the business side of things. Anshu notes that having business knowledge helps in creating useful machine-learning projects and contributing to the company's goals.

Exciting Areas to Focus: Anshu finds Natural Language Processing (NLP) and data engineering as promising areas in data science. These fields offer great opportunities for growth and innovation.

Read the full exclusive interview here (Available only for subscribers).