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Machine Learning for TimeSeries with Python: Forecast, predict, and detect anomalies with stateoftheart machine learning met,Used
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Become proficient in deriving insights from timeseries data and analyzing a model's performanceKey Features: Explore popular and modern machine learning methods including the latest online and deep learning algorithms Learn to increase the accuracy of your predictions by matching the right model with the right problem Master timeseries via realworld case studies on operations management, digital marketing, finance, and healthcareBook Description:Machine learning has emerged as a powerful tool to understand hidden complexities in timeseries datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for timeseries and builds your understanding of traditional autoregressive models as well as modern nonparametric models. You will become confident with loading timeseries datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.Machine Learning for TimeSeries with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes realworld case studies covering weather, traffic, biking, and stock market data.By the end of this book, you will be proficient in effectively analyzing timeseries datasets with machine learning principles.What You Will Learn: Understand the main classes of timeseries and learn how to detect outliers and patterns Choose the right method to solve timeseries problems Characterize seasonal and correlation patterns through autocorrelation and statistical techniques Get to grips with timeseries data visualization Understand classical timeseries models like ARMA and ARIMA Implement deep learning models like Gaussian processes and transformers and stateoftheart machine learning models Become familiar with many libraries like prophet, xgboost, and TensorFlowWho this book is for:This book is ideal for data analysts, data scientists, and Python developers who are looking to perform timeseries analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
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