Towards Data Science’s Post

Combining the power of TensorFlow, NeuralForecast, and zero-shot LLMs, Jinhang Jiang shows how we can generate accurate multi-step quantile forecasts.

🔑 Key Terms in Time Series Data: A Quick Guide! ⌛️📚 Hey #LinkedInNetwork! Let's dive into the key terms of Time Series Data! 🌊🔍 📈 Time Series: A sequence of data points collected at regular intervals over time. It's the foundation of time series analysis. ⏰📊 🔎 Trend: The long-term pattern or direction in which data points move. It helps identify overall growth or decline. 📈📉 🔄 Seasonality: Regular and predictable patterns that repeat over fixed intervals, such as daily, weekly, or yearly cycles. 🌞🌚 📉 Stationarity: When the statistical properties of a time series remain constant over time, such as mean and variance. It simplifies analysis. 📊🔒 🔮 Forecasting: Predicting future data points based on historical patterns and trends. It aids decision-making and planning. 💡🔍 📊 ARIMA: Autoregressive Integrated Moving Average. A popular model for time series analysis that considers past values and differencing. 🌐🧮 📈 LSTM: Long Short-Term Memory. A type of recurrent neural network used for modeling and forecasting time series data. 🧠💻 💡 Anomaly Detection: Identifying unusual or unexpected data points that deviate from the normal behavior of the time series. 🚩❌ Mastering these key terms opens up a world of possibilities for understanding and leveraging time series data! 🌟⌛️ #TimeSeriesData #Analytics #Forecasting #DataScience #KeyTerms

🌐 Navigating the World of High-Dimensional Data: A Comparative Study on PCA, t-SNE, and UMAP 🌐 In today’s data-driven world, we often work with vast, high-dimensional datasets that reveal valuable insights if visualized correctly. But how do we effectively reduce dimensions without losing crucial information? My recent research dives into three powerful dimensionality reduction techniques for data visualization: PCA (Principal Component Analysis), t-SNE (t-Distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection). Here’s a quick summary of each and what we discovered about their strengths and trade-offs: 1️⃣ PCA – The Linear Baseline 📌 Projects data onto principal components to capture maximum variance. 🔍 Best for: Simple, linear datasets where global structure matters more than local clustering. ⚡ Fastest computation, but struggles with non-linear relationships. 2️⃣ t-SNE – The Local Structure Specialist 📌 Preserves local relationships in data, ideal for highlighting clusters. 🔍 Best for: Complex datasets with distinct clusters (e.g., image and gene expression data). ⏱️ Trade-offs: Computationally intensive and distorts global layout, so distances between clusters might not reflect true relationships. 3️⃣ UMAP – Balancing Local and Global Structures 📌 Preserves local and global structures, offering fast computation with a balanced view. 🔍 Best for: Large, complex datasets where capturing both neighbourhood details and overall layout is essential. ⚙️ Great flexibility with parameter tuning, making it adaptable across diverse datasets. 🎯 Key Takeaway: Each method has its place depending on the dataset and the specific goals for visualization. While PCA provides a quick global view, t-SNE and UMAP are better suited for complex data, with UMAP striking the best balance between preserving local and global data structures. 💡 Future Directions: Exploring methods like autoencoders and Isomap for even better dimensionality reduction could enhance the way we visualize large, complex datasets. 👉 Read the full study for detailed insights and recommendations on dimensionality reduction techniques for visualization in fields like bioinformatics, NLP, and finance. Let’s keep pushing the boundaries of how we visualize and interpret data! 🔍📊 #DataScience #MachineLearning #DimensionalityReduction #PCA #tSNE #UMAP #DataVisualization #Research #DataInsights #LinkedInResearch #DataScienceCommunity

✨ 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 ✨ ----------------------------------------- Hello, Connections! Understanding 𝙥𝙧𝙤𝙗𝙖𝙗𝙞𝙡𝙞𝙩𝙮 is crucial for anyone diving into the world of data science. Here’s why it matters and how you can get started! 👇 🔍 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆? ----------------------------- Probability measures the likelihood that an event will occur. It’s a fundamental concept used to interpret and analyze data, making it essential for predicting trends and outcomes. 🎓 𝗞𝗲𝘆 𝗧𝗲𝗿𝗺𝘀: ------------------ * 𝙀𝙫𝙚𝙣𝙩: Any outcome or combination of outcomes. * 𝙎𝙖𝙢𝙥𝙡𝙚 𝙎𝙥𝙖𝙘𝙚: All possible outcomes of an experiment. * 𝙋(𝙀𝙫𝙚𝙣𝙩): The probability of an event occurring 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗧𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆: ------------------------------------- 1. 𝑩𝒊𝒏𝒐𝒎𝒊𝒂𝒍 𝑻𝒉𝒆𝒐𝒓𝒆𝒎 --------------------------- The Binomial Theorem is used to describe the distribution of binary outcomes (success/failure) over multiple trials. It’s fundamental for understanding processes that have two possible outcomes. 2. 𝑵𝒐𝒓𝒎𝒂𝒍 𝑫𝒊𝒔𝒕𝒓𝒊𝒃𝒖𝒕𝒊𝒐𝒏 ------------------------------ Also known as the Gaussian distribution, it’s a continuous probability distribution that is symmetrical around its mean. It’s crucial for modeling real-world phenomena where data tends to cluster around a central value. 3. 𝑷𝒐𝒊𝒔𝒔𝒐𝒏 𝑫𝒊𝒔𝒕𝒓𝒊𝒃𝒖𝒕𝒊𝒐𝒏 ------------------------------ This distribution is used to model the number of events occurring within a fixed interval of time or space. It’s particularly useful in fields like telecommunications, traffic engineering, and risk assessment. 4. 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔 𝑻𝒆𝒔𝒕𝒊𝒏𝒈 ----------------------------- A statistical method that uses sample data to evaluate a hypothesis about a population parameter. It’s a cornerstone in data-driven decision-making. 5. 𝑪𝒐𝒏𝒅𝒊𝒕𝒊𝒐𝒏𝒂𝒍 𝑷𝒓𝒐𝒃𝒂𝒃𝒊𝒍𝒊𝒕𝒚 ---------------------------------- This is the probability of an event occurring given that another event has already occurred. It’s essential for building predictive models and understanding dependencies between variables. 🧠 𝗪𝗵𝘆 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: --------------------------------------------------------- Predictive Modeling: Helps in building models that predict future outcomes. Risk Assessment: Assists in evaluating risks and making informed decisions. Data Analysis: Enhances the interpretation of data patterns and trends. 📚 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗥𝗲𝗮𝗱𝗶𝗻𝗴: “Introduction to Probability” by Joseph K. Blitzstein “The Signal and the Noise” by Nate Silver 🛎️ Follow for more Mosaddik Hussain Tagging these people for better reach- Krish Naik Nitish Singh Pradeep M Analytics Vidhya Alberto Ferrari #DataScience #Probability #Analytics #BigData #DataEngineering #MachineLearning #LinkedInLearning #DataAnalysis #PredictiveModeling #RiskAssessment #DataDriven

🌟 Unraveling Model Architectures: A Comparative Analysis of ARIMA and LSTM 🌟 Are you ready to dive into the fascinating world of time series forecasting? 📈 Today, let's embark on a journey through two powerful forecasting techniques: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory) networks. 🧠💡 In the realm of predictive analytics, choosing the right model is crucial, and understanding the strengths and weaknesses of each approach is paramount. So, let's break it down: 🔄 ARIMA (AutoRegressive Integrated Moving Average): ARIMA has long been a cornerstone in time series forecasting. Its simplicity, coupled with its ability to capture linear relationships in data, makes it a popular choice. By analyzing past values and errors, ARIMA can generate forecasts, making it suitable for stationary data with clear patterns. 🌐 LSTM (Long Short-Term Memory): Enter the realm of deep learning with LSTM networks. Unlike ARIMA, LSTM can capture intricate, non-linear relationships within sequences, making it ideal for data with complex patterns, trends, and long-term dependencies. Its ability to retain memory over time allows for more nuanced forecasting in dynamic environments. 📊 Comparative Analysis: Now, let's roll up our sleeves and compare these powerhouses. While ARIMA excels in simplicity and interpretability, LSTM shines in handling complex, high-dimensional data. ARIMA may struggle with non-linear relationships and long-term dependencies, whereas LSTM can adapt more dynamically to varying patterns. 💡 Key Takeaways: For simple, linear patterns, ARIMA might be your best bet. When dealing with complex, dynamic data, LSTM can offer superior performance. Always consider the nature of your data, the forecasting horizon, and computational resources when choosing a model. 🚀 Conclusion: In the ever-evolving landscape of predictive analytics, the choice between ARIMA and LSTM isn't always black and white. Understanding their nuances empowers us to make informed decisions, driving actionable insights and informed strategies. 🔍 Curious to learn more? Dive deeper into the world of time series forecasting and unlock the potential of your data! #ARIMA #LSTM #TimeSeries #DataScience #PredictiveAnalytics Let's spark a conversation! Which forecasting model do you prefer, and why? Share your thoughts below! 👇✨

Exploring segmentation in machine learning, Hussein Fellahi presents a novel solution that combines clustering and prediction in one optimized framework. The new Cluster While Classify method offers superior performance, especially in low data settings. Read more now!

🚀 Exploring Survival SVM I’ve recently started exploring Survival Support Vector Machines (Survival SVM), and I’m excited to share what I’ve learned so far! If you’re into predictive modeling for time-to-event data, this might be as intriguing to you as it is to me. // What is Survival SVM? Survival SVM is an adaptation of the classic Support Vector Machine (SVM) for survival analysis. Unlike traditional regression or classification, survival analysis focuses on predicting the time until an event occurs (e.g., machine failure, patient relapse, or customer churn). What makes Survival SVM stand out is its ability to handle censored data, cases where the event hasn’t happened yet for some observations. // The Role of Kernels One of the coolest aspects of Survival SVM is its use of kernel functions. Kernels allow the model to capture non-linear relationships in the data by transforming it into a higher-dimensional space. For example: - Linear Kernel: Simple and interpretable, great for linear relationships. - RBF Kernel: Powerful for capturing complex, non-linear patterns. - Polynomial Kernel: Useful for modeling interactions between features. Choosing the right kernel is key to unlocking the model’s potential, but it’s also something I’m still experimenting with. If you have tips or experiences to share about kernel selection, I’d love to hear them! // Why is Survival SVM Useful? 1. Handles Censored Data: It gracefully deals with incomplete data, which is common in real-world scenarios. 2. Flexible Modeling: Kernels make it versatile for capturing complex patterns. 3. High-Dimensional Data: It performs well even when the number of features is large. // Applications: - Healthcare: Predicting patient survival or disease progression. - Finance: Estimating time-to-default for loans. - Engineering: Predicting equipment failure or maintenance needs. // Challenges I’m Still Figuring Out: - Interpretability: Unlike traditional survival models like Cox Proportional Hazards, Survival SVM can be a bit of a "black box." - Hyperparameter Tuning: Balancing kernel choice, regularization, and other parameters is tricky but crucial for good performance. I’m still learning the ropes of Survival SVM, and I’d love to hear from others who have worked with it! What challenges have you faced? Any tips for someone just starting out? #MachineLearning #SurvivalAnalysis #DataScience #AI #SurvivalSVM #Kernels #LearningInPublic

Demystifying Classical Time Series Analysis Time series analysis is a cornerstone of data science, helping industries predict trends and optimize strategies. This article dives deep into the essential techniques and real-world applications of classical time series analysis, paving the way for better decision-making and forecasting. 🔍 Key Highlights: Fundamental techniques: ARIMA, Moving Averages, Exponential Smoothing. Practical applications: Predicting stock prices, weather patterns, and demand forecasting. Best practices for handling seasonal data and improving model accuracy. 🔗 Read the full article: https://lnkd.in/gfdWifhG Additional Resources: 1. Kaggle Time Series Datasets: https://www.kaggle.com 2. Python Time Series Libraries (GitHub): https://lnkd.in/gdkKfZ_S 3. Advanced Forecasting with Statsmodels: https://lnkd.in/gbHed_Dg #TimeSeriesAnalysis #DataScience #Forecasting #MachineLearning #AI

Excited to share our new work on Machine Learning! Andrea Failla, Rémy Cazabet, Giulio Rossetti SoBigData Research Infrastructure We reimagine group evolution in temporal data with "#archetypes" and "#facets". Just as a bird is more represented by a sparrow rather than a penguin, SOTA event types in #dynamic #clustering / #community #detection such as "merges" and "splits" are actually typical examples of a category. This is why we forget rigid events based on what one wishes to extract from the data to explore the reality of group evolution observed in data. Check out the paper for more! https://lnkd.in/dF9VcdGJ

Thank you @Sthepane Degeye for the amazing approach to Conformal Prediction in the field of Machine Learning. It is truly insightful! Check out more details here: https://lnkd.in/gc2YBs-q #conformalprediction #prediction #machinelearning