Tag: prediction
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Leveraging Predictive Uncertainties of Time Series Forecasting Models
Featured Image via Canva. Table of Contents Introduction Random Simulation Tests TSLA Stock 43 Days TSLA Stock 300 Days Housing in the United States Industrial Production Federal Funds Rate Data S&P 500 Absolute Returns Number of Airline Passengers- 1. Holt-Winters Number of Airline Passengers- 2. Prophet Average Temperature in India Monthly Sales Data Analysis QC…
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Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM
The data science project involves evaluating various sales forecasting algorithms in Python using a Kaggle time-series dataset. The forecasting algorithms include tslearn, Random Walk, Holt-Winters, SARIMA, GARCH, Prophet, LSTM and Di Pietro’s Model. The goal is to predict next month’s sales for a list of shops and products, which slightly changes every month. The best…
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Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM
We predict the Remaining Useful Life (RUL) of NASA turbofan jet engines by comparing the statsmodels OLS, ML SciKit-Learn regression vs LSTM Keras in Python. The input dataset is the Kaggle version of the public dataset for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.
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A Balanced Mix-and-Match Time Series Forecasting: ThymeBoost, Prophet, and AutoARIMA
The post evaluates the performance of popular Time Series Forecasting (TSF) methods, namely AutoARIMA, Facebook Prophet, and ThymeBoost on four real-world time series datasets: Air Passengers, U.S. Wholesale Price Index (WPI), BTC-USD price, and Peyton Manning. Each TSF model uses historical data to identify trends and make future predictions. Studies indicate that ThymeBoost, which combines…
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NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading
The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…
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Machine Learning-Based Crop Yield Prediction, Classification, and Recommendations
We have implemented a Machine Learning-Based decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops.
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Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction
This post discusses the prediction of stroke using machine learning (ML) models, focusing on the use of early warning systems and data balancing techniques to manage the highly imbalanced stroke data. It includes a detailed exploration of the torch artificial neural network training and performance evaluation, as well as the implementation and evaluation of various…
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Using AI/ANN AUC>90% for Early Diagnosis of Cardiovascular Disease (CVD)
The project utilizes AI-driven cardiovascular medicine with a focus on early diagnosis of heart disease using Artificial Neural Networks (ANN). Aiming to improve early detection of heart issues, the project processed a dataset of 303 patients using Python libraries and conducted extensive exploratory data analysis. A Sequential ANN model was subsequently built, revealing excellent performance…
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SARIMAX Crude Oil Prices Forecast – 1. WTI
The content discusses a detailed forecast of Brent and WTI oil prices for 2023, using Python, SARIMAX and Time Series Analysis. The data indicates volatility in the oil market starting 2023, with prices set to decrease from 2022 levels. Experts also warn of a potential US recession in 2023, which could further impact the oil…
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The Zacks Market Outlook Nov ’22 – Energy
Featured Image by Canva. Let’s review the current Energy Market Outlook to power your investment portfolio with Zack Research. Indeed, Energy is at the heart of development. Energy makes possible the investments, innovations, and new industries that are the engines of jobs, inclusive growth, and shared prosperity for entire economies. What Rapidly Shifting Energy Markets…
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BTC-USD Freefall vs FB/Meta Prophet 2022-23 Predictions
It is widely recognized that crypto price prediction is considered a very challenging task, due to its chaotic (highly volatile) and very complex nature. Nethertheless, accurate predictions can assist crypto investors towards right investing decisions. Why not Predicting BTC-USD Prices Using Facebook’s Prophet? Let’s discuss this state-of-the-art model within the context of BTC-USD 2022-23 price prediction…
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BTC-USD Price Prediction with LSTM
The objective of this project is to test the deep learning algorithm of real-time BTC-USD price prediction. We trained the 2-layers Long Short Term Memory Neural Network using Bitcoin Historical Data. The trained LSTM model can be used to predict future price movements of bitcoin. RMSE ~ $64, with the mean price of $20k (Oct…
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A Comparison of ML/AI Diabetes-2 Binary Classification Algorithms
The post discusses the increasing urgency to diagnose and treat Type-2 Diabetes (T2D), particularly in developing nations. It delves into the use of data-driven techniques, including ML/AI, in processing T2D data. Different ML/AI methods including DNN, SVM, and DT are applied to the Kaggle PIMA Indian Diabetes (PID) dataset, and performance is assessed using Python…
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HealthTech ML/AI Q3 ’22 Round-Up
Featured Photo by Andy Kelly on Unsplash This blog presents a Q3 ’22 summary of current healthtech ML/AI innovation methods, trends and challenges. Virtual reality, artificial intelligence, augmented reality, and machine learning are all healthcare technology trends that are going to play a vital role across the entire healthcare system. Let’s take a look at…
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ANOVA-OLS Prediction of Surgical Volumes
Operating rooms (ORs) are some of the most valuable hospital assets, generating a large part of hospital revenue. Statistical models have been developed using datasets to predict daily surgical volumes weeks in advance. We focus on the VUMC dataset for evaluation of our statistical models. We use the ANOVA null-hypothesis test for the total number…
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Predicting Trend Reversal in Algorithmic Trading using Stochastic Oscillator in Python
This is the example stochastic oscillator in Python for algorithmic trading $NVIDIA candlestick chart vs a stochastic oscillator chart over our trading period.
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Stock Forecasting with FBProphet
Prophet from Meta (Facebook) is a procedure for forecasting time series data such as stocks. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
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ML/AI Regression for Stock Prediction – AAPL Use Case
1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise 3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output
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Keras LSTM Stock Prediction
Despite the vast amount of historical stock data, the high noise to signal ratio and varied market conditions cause inadequate stock price predictions. A solution to these challenges is Long Short Term Memory (LSTM). LSTMs offer a range of adjustable parameters without the necessity for fine-tuning, and efficiently handle sequential data.
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