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Tag: TimeSeries

  • Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

    Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods


    Price: $31.81
    (as of Dec 25,2024 22:34:50 UTC – Details)


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    ASIN ‏ : ‎ B09GS44ZP4
    Publisher ‏ : ‎ Packt Publishing; 1st edition (October 29, 2021)
    Publication date ‏ : ‎ October 29, 2021
    Language ‏ : ‎ English
    File size ‏ : ‎ 16832 KB
    Text-to-Speech ‏ : ‎ Enabled
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 370 pages


    Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

    In today’s data-driven world, time-series data is everywhere, from stock prices and weather forecasts to sensor data and sales projections. Machine learning techniques have revolutionized the way we analyze and interpret time-series data, allowing us to forecast future trends, predict outcomes, and detect anomalies with unprecedented accuracy.

    In this post, we will explore how to leverage Python and state-of-the-art machine learning methods to tackle time-series data analysis. We will cover techniques such as ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Prophet for forecasting, as well as isolation forests and one-class SVM for anomaly detection.

    By the end of this post, you will have a solid understanding of how to apply machine learning to time-series data, enabling you to make better predictions, optimize resources, and detect anomalies in your data. Stay tuned for practical examples, code snippets, and hands-on exercises to help you master these powerful techniques. Let’s dive in and unlock the potential of machine learning for time-series data analysis!
    #Machine #Learning #TimeSeries #Python #Forecast #predict #detect #anomalies #stateoftheart #machine #learning #methods

  • Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks: A Deep Learning approach for forecasting stock price time-series data in groups

    Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks: A Deep Learning approach for forecasting stock price time-series data in groups


    Price: $47.00
    (as of Dec 24,2024 19:24:10 UTC – Details)




    Publisher ‏ : ‎ LAP LAMBERT Academic Publishing (July 30, 2021)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 52 pages
    ISBN-10 ‏ : ‎ 620419092X
    ISBN-13 ‏ : ‎ 978-6204190921
    Item Weight ‏ : ‎ 3.39 ounces
    Dimensions ‏ : ‎ 5.91 x 0.12 x 8.66 inches


    In today’s fast-paced and volatile stock market environment, accurately forecasting stock prices is essential for making informed investment decisions. Traditional forecasting methods often fall short when it comes to capturing the complex patterns and trends in stock price time-series data. However, with the advancement of deep learning techniques such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, forecasting stock prices has become more accurate and reliable.

    LSTM and GRU neural networks are specifically designed to handle sequential data and are well-suited for time-series forecasting tasks. By leveraging the memory capabilities of these networks, we can effectively capture long-term dependencies and patterns in stock price data, making them ideal for forecasting future stock prices.

    In this post, we will explore how LSTM and GRU neural networks can be used to accurately forecast stock prices in groups. By grouping stocks based on similar characteristics or industry sectors, we can improve the forecasting accuracy by capturing common trends and patterns within each group.

    To start, we will preprocess and normalize the stock price time-series data for each group. We will then train LSTM and GRU neural networks on historical stock price data to learn the underlying patterns and trends. By fine-tuning the network parameters and optimizing the model architecture, we can improve the forecasting accuracy and reduce prediction errors.

    Once the models are trained and validated, we can use them to forecast future stock prices for each group. By comparing the predicted prices with the actual prices, we can evaluate the accuracy of the forecasting models and make adjustments as needed.

    Overall, using LSTM and GRU neural networks for forecasting stock prices in groups offers a powerful and effective approach to capturing the complex dynamics of the stock market. By leveraging the memory capabilities of these networks, we can improve the accuracy and reliability of stock price forecasts, enabling investors to make more informed decisions and maximize their returns.
    #Accurately #Forecasting #Stock #Prices #LSTM #GRU #Neural #Networks #Deep #Learning #approach #forecasting #stock #price #timeseries #data #groups

  • Machine Learning For Time-Series With Python By Ben Auffarth 2021

    Machine Learning For Time-Series With Python By Ben Auffarth 2021



    Machine Learning For Time-Series With Python By Ben Auffarth 2021

    Price : 77.00

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    Machine Learning For Time-Series With Python By Ben Auffarth 2021

    In this post, we will explore the fascinating world of time-series analysis using machine learning techniques with Python. Ben Auffarth, a seasoned data scientist and machine learning expert, will guide us through the process of building predictive models for time-series data.

    Time-series data is everywhere – from stock prices and weather forecasts to sensor readings and customer behavior. Understanding and predicting patterns in time-series data can provide valuable insights for businesses and organizations.

    Ben Auffarth will walk us through the steps of preprocessing time-series data, selecting the right machine learning algorithms, and evaluating our models for accuracy and performance. We will cover popular algorithms such as ARIMA (AutoRegressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Prophet.

    By the end of this post, you will have a solid foundation in using machine learning for time-series analysis with Python. Whether you are a beginner looking to learn the basics or an experienced data scientist seeking to expand your skill set, this post is for you.

    Stay tuned for actionable insights, practical examples, and hands-on tutorials from Ben Auffarth on Machine Learning For Time-Series With Python in 2021.
    #Machine #Learning #TimeSeries #Python #Ben #Auffarth

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