Univariate Stock Market Forecasting using Facebook Prophet in Python

Have you ever wondered how Facebook predicts the future? Meet Facebook Prophet, the open-source time series forecasting tool developed by Facebook’s Core Data Science team. Built on top of the PyStan library, Facebook Prophet offers a simple and intuitive interface for creating forecasts using historical data. What sets Facebook Prophet apart is its highly modular … Read more

Unveiling Hidden Patterns in the Cryptocurrency Market with Affinity Propagation and Python

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Affinity propagation is a powerful unsupervised clustering technique that can identify hidden patterns in large datasets. In the cryptocurrency world, where new coins are constantly emerging and prices can be highly volatile, affinity propagation can help investors simplify the chaos. By analyzing historical price data, affinity propagation groups coins into clusters based on their past … Read more

Stock Market Forecasting Neural Networks for Multi-Output Regression in Python

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Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed … Read more

Automate Crypto Trading with a Python-Powered Twitter Bot and Gate.io Signals

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This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple … Read more

Forecasting Beer Sales with ARIMA in Python

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Time series analysis and forecasting is a tough nut to crack, but the ARIMA model has been cracking it for decades. ARIMA, short for “Auto-Regressive Integrated Moving Average,” is a powerful statistical modeling technique for time series analysis. It’s particularly effective when the time series you’re analyzing follows a clear pattern, like seasonal changes in … Read more

Mastering Multivariate Stock Market Prediction with Python: A Guide to Effective Feature Engineering Techniques

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Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! While traditional time series data provides valuable insights into historical trends, multivariate forecasting models utilize additional features to identify patterns and predict future price movements. This process, known as “feature engineering,” is a crucial step … Read more

Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python

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Regression models based on recurrent neural networks (RNN) can recognize patterns in time series data, making them an exciting technology for stock market forecasting. What distinguishes these RNNs from traditional neural networks is their architecture. It consists of multiple layers of long-term, short-term memory (LSTM). These LSTM layers allow the model to learn patterns in … Read more

Measuring Regression Errors with Python

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Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models does not exist. Instead, there … Read more

Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in Python

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Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more

Stock Market Prediction – Adjusting Time Series Prediction Intervals in Python

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Get ready to level up your time-series forecasting game! In this tutorial, we’re going to take things up a notch by showing you how to adjust prediction intervals using Keras recurrent neural networks and Python. Now, you may remember our previous article on stock market forecasting where we made a forecast for the S&P500 stock … Read more

Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with Python

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Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there has been increasing interest in using machine learning and deep learning techniques to generate predictions, in addition to traditional methods such as technical and fundamental analysis. Python libraries like Keras and Scikit-Learn make it relatively … Read more