Kulwinder singh parmar (11 risultati)

Condizione: New.

Paperback. Condizione: Brand New. 88 pages. 8.66x5.91x0.20 inches. In Stock.

Taschenbuch. Condizione: Neu. Invariant Subspaces Of Normal - Like Operators | Kulwinder Singh Parmar | Taschenbuch | 88 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139868674 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Paperback. Condizione: new. Paperback. Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence. Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters' models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka. By the end of this book, you will be a competent practitioner capable of building high-performance forecasting pipelines for stock prices, demand, and sensor data. You will possess the technical expertise to deploy scalable, ethical, and accurate models in real-world cloud environments with confidence.What you will learn Diagnose trend and seasonality using Statsmodels stationarity. Build ARIMA/SARIMA and smoothing models using Statsmodels. Engineer lag, rolling, and calendar-based forecasting features. Deploy FastAPI pipelines and monitor Kafka drift. Build LSTM and GRU architectures with TensorFlow. Backtest, compare, and ensemble models with confidence. Deploy, monitor, and retrain forecasting pipelines at scale.Who this book is forThis book is designed for data scientists, machine learning engineers, and analysts mastering temporal data. Proficiency in Python and basic statistics is required, while experience with cloud deployment or deep learning helps professional engineers scale models using the featured technical frameworks.Table of Contents1. Introduction to Time Series Data and Analysis2. Data Pre-processing and Feature Engineering3. Exploratory and Statistical Analysis of Time Series4. Autoregressive Models5. Moving Average and ARMA Models6. ARIMA and SARIMA Models7. Exponential Smoothing Methods8. Feature-based Machine Learning for Time Series Forecasting9. Introduction to Deep Learning for Time Series10. Building and Training LSTM Models for Time Series11. Advanced Deep Learning Architectures and Multivariate Forecasting12. Multivariate Time Series Forecasting13. Model Evaluation, Selection, and Ensembling14. Forecasting at Scale and Model Deployment15. Time Series Forecasting in Practice This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -There is an outstanding problem in operator theory, the so-called, INVARIANT SUBSPACE PROBLEM: Given a complex Banach space X, which operators on X have non-trivial closed invariant subspaces This problem has been open for more than half a century. In spite of momentous efforts by functional analysts, the problem continues to elude them even today. Until fairly recently, it was not known whether there was any operator T without a non-trivial closed invariant subspace. 88 pp. Englisch.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - There is an outstanding problem in operator theory, the so-called, INVARIANT SUBSPACE PROBLEM: Given a complex Banach space X, which operators on X have non-trivial closed invariant subspaces This problem has been open for more than half a century. In spite of momentous efforts by functional analysts, the problem continues to elude them even today. Until fairly recently, it was not known whether there was any operator T without a non-trivial closed invariant subspace.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Parmar Kulwinder SinghDr Kulwinder Singh Parmar is an assistant Professor at Department of Mathematics, IKG Punjab Technical University, Jalandhar,Punjab. He did his Ph.D at Guru Gobind Singh Indraprastha University. He actively invo.…

Paperback. Condizione: new. Paperback. Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence. Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters' models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka. By the end of this book, you will be a competent practitioner capable of building high-performance forecasting pipelines for stock prices, demand, and sensor data. You will possess the technical expertise to deploy scalable, ethical, and accurate models in real-world cloud environments with confidence.What you will learn Diagnose trend and seasonality using Statsmodels stationarity. Build ARIMA/SARIMA and smoothing models using Statsmodels. Engineer lag, rolling, and calendar-based forecasting features. Deploy FastAPI pipelines and monitor Kafka drift. Build LSTM and GRU architectures with TensorFlow. Backtest, compare, and ensemble models with confidence. Deploy, monitor, and retrain forecasting pipelines at scale.Who this book is forThis book is designed for data scientists, machine learning engineers, and analysts mastering temporal data. Proficiency in Python and basic statistics is required, while experience with cloud deployment or deep learning helps professional engineers scale models using the featured technical frameworks.Table of Contents1. Introduction to Time Series Data and Analysis2. Data Pre-processing and Feature Engineering3. Exploratory and Statistical Analysis of Time Series4. Autoregressive Models5. Moving Average and ARMA Models6. ARIMA and SARIMA Models7. Exponential Smoothing Methods8. Feature-based Machine Learning for Time Series Forecasting9. Introduction to Deep Learning for Time Series10. Building and Training LSTM Models for Time Series11. Advanced Deep Learning Architectures and Multivariate Forecasting12. Multivariate Time Series Forecasting13. Model Evaluation, Selection, and Ensembling14. Forecasting at Scale and Model Deployment15. Time Series Forecasting in Practice This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -There is an outstanding problem in operator theory, the so-called, INVARIANT SUBSPACE PROBLEM: Given a complex Banach space X, which operators on X have non-trivial closed invariant subspaces This problem has been open for more than half a century. In spite of momentous efforts by functional analysts, the problem continues to elude them even today. Until fairly recently, it was not known whether there was any operator T without a non-trivial closed invariant subspace.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch.…