Download or read book TIME SERIES ANALYSIS FORECASTING STOCK PRICE USING MACHINE LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-02 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock trading and financial instrument markets offer significant opportunities for wealth creation. The ability to predict stock price movements has long intrigued researchers and investors alike. While some theories, like the Efficient Market Hypothesis, suggest that consistently beating the market is nearly impossible, others contest this viewpoint. Stock price prediction involves forecasting the future value of a given stock. In this project, we focus on the S&P 500 Index, which consists of 500 stocks from various sectors of the US economy and serves as a key indicator of US equities. To tackle this task, we utilize the Yahoo stock price history dataset, which contains 1825 rows and 7 columns including Date, High, Low, Open, Close, Volume, and Adj Close. To enhance our predictions, we incorporate technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, for the forecasting task, we employ various regression algorithms including Linear Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Naïve Bayes Regression, K-Nearest Neighbor Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, MLP Regression, Lasso Regression, and Ridge Regression. These models aim to predict the future Adj Close price of the stock based on historical data. In addition to stock price prediction, we also delve into predicting stock daily returns using machine learning models. We utilize K-Nearest Neighbor Classifier, Random Forest Classifier, Naive Bayes Classifier, Logistic Regression Classifier, Decision Tree Classifier, Support Vector Machine Classifier, LGBM Classifier, Gradient Boosting Classifier, XGB Classifier, MLP Classifier, and Extra Trees Classifier. These models are trained to predict the direction of daily stock returns (positive or negative) based on various features and technical indicators. To assess the performance of these machine learning models, we evaluate several important metrics. Accuracy measures the overall correctness of the predictions, while recall quantifies the ability to correctly identify positive cases (upward daily returns). Precision evaluates the precision of positive predictions, and the F1 score provides a balanced measure of precision and recall. Additionally, we consider macro average, which calculates the average metric value across all classes, and weighted average, which provides a balanced representation considering class imbalances. To enhance the user experience and facilitate data exploration, we develop a graphical user interface (GUI). The GUI is built using PyQt and offers an interactive platform for users to visualize and interact with the data. It provides features such as plotting boundary decisions, visualizing feature distributions and importance, comparing predicted values with true values, displaying confusion matrices, learning curves, model performance, and scalability analysis. The GUI allows users to customize the analysis by selecting different models, time periods, or variables of interest, making it accessible and user-friendly for individuals without extensive programming knowledge. The combination of exploring the dataset, forecasting stock prices, predicting daily returns, and developing a GUI creates a comprehensive framework for analyzing and understanding stock market trends. By leveraging machine learning algorithms and evaluating performance metrics, we gain valuable insights into the accuracy and effectiveness of our predictions. The GUI further enhances the accessibility and usability of the analysis, enabling users to make data-driven decisions and explore the stock market with ease.

Download or read book GOOGLE STOCK PRICE TIME SERIES ANALYSIS VISUALIZATION FORECASTING AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-06-11 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Google, officially known as Alphabet Inc., is an American multinational technology company. It was founded in September 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University. Initially, it started as a research project to develop a search engine, but it rapidly grew into one of the largest and most influential technology companies in the world. Google is primarily known for its internet-related services and products, with its search engine being its most well-known offering. It revolutionized the way people access information by providing a fast and efficient search engine that delivers highly relevant results. Over the years, Google expanded its portfolio to include a wide range of products and services, including Google Maps, Google Drive, Gmail, Google Docs, Google Photos, Google Chrome, YouTube, and many more. In addition to its internet services, Google ventured into hardware with products like the Google Pixel smartphones, Google Home smart speakers, and Google Nest smart home devices. It also developed its own operating system called Android, which has become the most widely used mobile operating system globally. Google's success can be attributed to its ability to monetize its services through online advertising. The company introduced Google AdWords, a highly successful online advertising program that enables businesses to display ads on Google's search engine and other websites through its AdSense program. Advertising contributes significantly to Google's revenue, along with other sources such as cloud services, app sales, and licensing fees. The dataset used in this project starts from 19-Aug-2004 and is updated till 11-Oct-2021. It contains 4317 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. You can download the dataset from https://viviansiahaan.blogspot.com/2023/06/google-stock-price-time-series-analysis.html. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, you will learn how to perform forecasting based on regression on Adj Close price of Google stock price, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression. The machine learning models used to predict Google daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Download or read book Applied Time Series Analysis and Forecasting with Python written by Changquan Huang and published by Springer Nature. This book was released on 2022-10-19 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

Download or read book DATA VISUALIZATION TIME SERIES FORECASTING AND PREDICTION USING MACHINE LEARNING WITH TKINTER written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-09-06 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project is a comprehensive and multifaceted application that leverages data visualization, time-series forecasting, and machine learning techniques to gain insights into bitcoin data and make predictions. This project serves as a valuable tool for financial analysts, traders, and investors seeking to make informed decisions in the stock market. The project begins with data visualization, where historical bitcoin market data is visually represented using various plots and charts. This provides users with an intuitive understanding of the data's trends, patterns, and fluctuations. Features distribution analysis is conducted to assess the statistical properties of the dataset, helping users identify key characteristics that may impact forecasting and prediction. One of the project's core functionalities is time-series forecasting. Through a user-friendly interface built with Tkinter, users can select a stock symbol and specify the time horizon for forecasting. The project supports multiple machine learning regressors, such as Linear Regression, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, Lasso, Ridge, AdaBoost, and KNN, allowing users to choose the most suitable algorithm for their forecasting needs. Time-series forecasting is crucial for making predictions about stock prices, which is essential for investment strategies. The project employs various machine learning regressors to predict the adjusted closing price of bitcoin stock. By training these models on historical data, users can obtain predictions for future adjusted closing prices. This information is invaluable for traders and investors looking to make buy or sell decisions. The project also incorporates hyperparameter tuning and cross-validation to enhance the accuracy of these predictions. These models employ metrics such as Mean Absolute Error (MAE), which quantifies the average absolute discrepancy between predicted values and actual values. Lower MAE values signify superior model performance. Additionally, Mean Squared Error (MSE) is used to calculate the average squared differences between predicted and actual values, with lower MSE values indicating better model performance. Root Mean Squared Error (RMSE), derived from MSE, provides insights in the same units as the target variable and is valued for its lower values, denoting superior performance. Lastly, R-squared (R2) evaluates the fraction of variance in the target variable that can be predicted from independent variables, with higher values signifying better model fit. An R2 of 1 implies a perfect model fit. In addition to close price forecasting, the project extends its capabilities to predict daily returns. By implementing grid search, users can fine-tune the hyperparameters of machine learning models such as Random Forests, Gradient Boosting, Support Vector, Decision Tree, Gradient Boosting, Extreme Gradient Boosting, Multi-Layer Perceptron, and AdaBoost Classifiers. This optimization process aims to maximize the predictive accuracy of daily returns. Accurate daily return predictions are essential for assessing risk and formulating effective trading strategies. Key metrics in these classifiers encompass Accuracy, which represents the ratio of correctly predicted instances to the total number of instances, Precision, which measures the proportion of true positive predictions among all positive predictions, and Recall (also known as Sensitivity or True Positive Rate), which assesses the proportion of true positive predictions among all actual positive instances. The F1-Score serves as the harmonic mean of Precision and Recall, offering a balanced evaluation, especially when considering the trade-off between false positives and false negatives. The ROC Curve illustrates the trade-off between Recall and False Positive Rate, while the Area Under the ROC Curve (AUC-ROC) summarizes this trade-off. The Confusion Matrix provides a comprehensive view of classifier performance by detailing true positives, true negatives, false positives, and false negatives, facilitating the computation of various metrics like accuracy, precision, and recall. The selection of these metrics hinges on the project's specific objectives and the characteristics of the dataset, ensuring alignment with the intended goals and the ramifications of false positives and false negatives, which hold particular significance in financial contexts where decisions can have profound consequences. Overall, the "Data Visualization, Time-Series Forecasting, and Prediction using Machine Learning with Tkinter" project serves as a powerful and user-friendly platform for financial data analysis and decision-making. It bridges the gap between complex machine learning techniques and accessible user interfaces, making financial analysis and prediction more accessible to a broader audience. With its comprehensive features, this project empowers users to gain insights from historical data, make informed investment decisions, and develop effective trading strategies in the dynamic world of finance. You can download the dataset from: http://viviansiahaan.blogspot.com/2023/09/data-visualization-time-series.html.

Download or read book Modern Time Series Forecasting with Python written by Manu Joseph and published by Packt Publishing Ltd. This book was released on 2022-11-24 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Download or read book STOCK PRICE ANALYSIS PREDICTION AND FORECASTING USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2022-05-27 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dataset is a playground for fundamental and technical analysis. It is said that 30% of traffic on stocks is already generated by machines, can trading be fully automated? If not, there is still a lot to learn from historical data. The dataset consists of data spans from 2010 to the end 2016, for companies new on stock market date range is shorter. To perform forecasting based on regression adjusted closing price of gold, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, and LSTM (Long-Short Term Memory) regression. The machine learning models used predict gold daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, Gaussian Mixture Model classifier, and Extra Trees classifier. Finally, you will plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Download or read book Introduction to Time Series Forecasting With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2017-02-16 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Download or read book Time Series Forecasting in Python written by Marco Peixeiro and published by Simon and Schuster. This book was released on 2022-10-04 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Download or read book Machine Learning for Time Series with Python written by Ben Auffarth and published by Packt Publishing Ltd. This book was released on 2021-10-29 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get better insights from time-series data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series. What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome 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 want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

Download or read book Machine Learning for Time Series Forecasting with Python written by Francesca Lazzeri and published by John Wiley & Sons. This book was released on 2020-12-15 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Download or read book Mastering Time Series Analysis and Forecasting with Python written by Sulekha Aloorravi and published by Orange Education Pvt Ltd. This book was released on 2024-03-26 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate. Key Features ● Dive into time series analysis fundamentals, progressing to advanced Python techniques. ● Gain practical expertise with real-world datasets and hands-on examples. ● Strengthen skills with code snippets, exercises, and projects for deeper understanding. Book Description "Mastering Time Series Analysis and Forecasting with Python" is an essential handbook tailored for those seeking to harness the power of time series data in their work. The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection. Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains. Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data. What you will learn ● Understand the fundamentals of time series data, including temporal patterns, trends, and seasonality. ● Proficiently utilize Python libraries such as pandas, NumPy, and matplotlib for efficient data manipulation and visualization. ● Conduct exploratory analysis of time series data, including identifying patterns, detecting anomalies, and extracting meaningful features. ● Build accurate and reliable predictive models using a variety of machine learning and deep learning techniques, including ARIMA, LSTM, and CNN. ● Perform multivariate and multiple time series forecasting, allowing for more comprehensive analysis and prediction across diverse datasets. ● Evaluate model performance using a range of metrics and validation techniques, ensuring the reliability and robustness of predictive models. Table of Contents 1. Introduction to Time Series 2. Overview of Time Series Libraries in Python 3. Visualization of Time Series Data 4. Exploratory Analysis of Time Series Data 5. Feature Engineering on Time Series 6. Time Series Forecasting – ML Approach Part 1 7. Time Series Forecasting – ML Approach Part 2 8. Time Series Forecasting - DL Approach 9. Multivariate Time Series, Metrics, and Validation Index

Download or read book CRYPTOCURRENCY PRICE ANALYSIS PREDICTION AND FORECASTING USING MACHINE LEARNING WITH PYTHON written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-21 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this project, we will be conducting a comprehensive analysis, prediction, and forecasting of cryptocurrency prices using machine learning with Python. The dataset we will be working with contains historical cryptocurrency price data, and our main objective is to build models that can accurately predict future price movements and daily returns. The first step of the project involves exploring the dataset to gain insights into the structure and contents of the data. We will examine the columns, data types, and any missing values present. After that, we will preprocess the data, handling any missing values and converting data types as needed. This will ensure that our data is clean and ready for analysis. Next, we will proceed with visualizing the dataset to understand the trends and patterns in cryptocurrency prices over time. We will create line plots, box plot, violin plot, and other visualizations to study price movements, trading volumes, and volatility across different cryptocurrencies. These visualizations will help us identify any apparent trends or seasonality in the data. To gain a deeper understanding of the time-series nature of the data, we will conduct time-series analysis year-wise and month-wise. This analysis will involve decomposing the time-series into its individual components like trend, seasonality, and noise. Additionally, we will look for patterns in price movements during specific months to identify any recurring seasonal effects. To enhance our predictions, we will also incorporate technical indicators into our analysis. Technical indicators, such as moving averages, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD), provide valuable information about price momentum and market trends. These indicators can be used as additional features in our machine learning models. With a strong foundation of data exploration, visualization, and time-series analysis, we will now move on to building machine learning models for forecasting the closing price of cryptocurrencies. We will utilize algorithms like Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, Multi-Layer Perceptron Regression, Lasso Regression, and Ridge Regression to make forecasting. By training our models on historical data, they will learn to recognize patterns and make predictions for future price movements. As part of our machine learning efforts, we will also develop models for predicting daily returns of cryptocurrencies. Daily returns are essential indicators for investors and traders, as they reflect the percentage change in price from one day to the next. By using historical price data and technical indicators as input features, we can build models that forecast daily returns accurately. Throughout the project, we will perform extensive hyperparameter tuning using techniques like Grid Search and Random Search. This will help us identify the best combinations of hyperparameters for each model, optimizing their performance. To validate the accuracy and robustness of our models, we will use various evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. These metrics will provide insights into the model's ability to predict cryptocurrency prices accurately. In conclusion, this project on cryptocurrency price analysis, prediction, and forecasting is a comprehensive exploration of using machine learning with Python to analyze and predict cryptocurrency price movements. By leveraging data visualization, time-series analysis, technical indicators, and machine learning algorithms, we aim to build accurate and reliable models for predicting future price movements and daily returns. The project's outcomes will be valuable for investors, traders, and analysts looking to make informed decisions in the highly volatile and dynamic world of cryptocurrencies. Through rigorous evaluation and validation, we strive to create robust models that can contribute to a better understanding of cryptocurrency market dynamics and support data-driven decision-making.

Download or read book Stock Market Prediction Using Machine Learning written by Prince Vipulbhai Patel and published by . This book was released on 2021 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market trading has gained popularity in today's world with the advancement in technology and social media. With the help of today's technology we can aim to predict the stock market for the future value of stocks. To make informed predictions, time series analysis is used by most stock brokers around the world. This paper explains and analyzes the prediction of a stock by using machine learning. In this paper, I propose a machine learning approach that will be trained from the available stock data by using acquired knowledge for a prediction with accuracy. In this context, the study will use a machine learning technique called Support Vector Machine (SVM) and Long Short term memory (LSTM) to predict stock prices.

Download or read book Stock Market Price Prediction using Machine Learning Techniques written by Mahfuz Islam Khan Jabed and published by Ocleno. This book was released on 2024-02-16 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Download or read book Ultimate Enterprise Data Analysis and Forecasting using Python written by Shanthababu Pandian and published by Orange Education Pvt Ltd. This book was released on 2023-12-28 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making KEY FEATURES ● Comprehensive Resource for Python-Based Time Series Analysis and Forecasting. ● Delve into real-world applications with industry-specific case studies. ● Extract valuable insights by solving time series challenges across various sectors. ● Understand the significance of Azure Time Series Insights and AWS Forecast components. ● Practical insights into leveraging cloud platforms for efficient time series forecasting. DESCRIPTION Embark on a transformative journey through the intricacies of time series analysis and forecasting with this comprehensive handbook. Beginning with the essential packages for data science and machine learning projects you will delve into Python's prowess for efficient time series data analysis, exploring the core components and real-world applications across various industries through compelling use-case studies. From understanding classical models like AR, MA, ARMA, and ARIMA to exploring advanced techniques such as exponential smoothing and ETS methods, this guide ensures a deep understanding of the subject. It will help you navigate the complexities of vector autoregression (VAR, VMA, VARMA) and elevate your skills with a deep dive into deep learning techniques for time series analysis. By the end of this book, you will be able to harness the capabilities of Azure Time Series Insights and explore the cutting-edge AWS Forecast components, unlocking the cloud's power for advanced and scalable time series forecasting. WHAT WILL YOU LEARN ● Explore Time Series Data Analysis and Forecasting, covering components and significance. ● Gain a practical understanding through hands-on examples and real-world case studies. ● Master Time Series Models (AR, MA, ARMA, ARIMA, VAR, VMA, VARMA) with executable samples. ● Delve into Deep Learning for Time Series Analysis, demystified with classical examples. ● Actively engage with Azure Time Series Insights and AWS Forecast components for a contemporary perspective. WHO IS THIS BOOK FOR? This book caters to beginners, intermediates, and practitioners in data-related fields such as Data Analysts, Data Scientists, and Machine Learning Engineers, as well as those venturing into Time Series Analysis and Forecasting. It assumes readers have a foundational understanding of programming languages (C, C++, Python), data structures, statistics, and visualization concepts. With a focus on specific projects, it also functions as a quick reference for advanced users. TABLE OF CONTENTS 1. Introduction to Python and its key packages for DS and ML Projects 2. Python for Time Series Data Analysis 3. Time Series Analysis and its Components 4. Time Series Analysis and Forecasting Opportunities in Various Industries 5. Exploring various aspects of Time Series Analysis and Forecasting 6. Exploring Time Series Models - AR, MA, ARMA, and ARIMA 7. Understanding Exponential Smoothing and ETS Methods in TSA 8. Exploring Vector Autoregression and its Subsets (VAR, VMA, and VARMA) 9. Deep Learning for Time Series Analysis and Forecasting 10. Azure Time Series Insights 11. AWSForecast Index

Download or read book Practical Time Series Analysis written by Dr. Avishek Pal and published by Packt Publishing Ltd. This book was released on 2017-09-28 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.