Tag: data-driven technology

  • Sensitivity of Kalman Object Tracking to Noise & State Errors

    Sensitivity of Kalman Object Tracking to Noise & State Errors

    Featured Foto by AtHul K Anand on Pexels Table of Contents Constant Voltage Alternating Current Linear Trend Path Non-Linear Trend Path Velocity Modeling Acceleration Modeling Circular Motion Robot 2D Location Tracking Notations: (dX, dY) = (XY) position error, (dVx, dVy) = (XY) velocity vector error, e.g. 3D Location Tracking Summary Explore More References

  • Malware Detection & Interpretation – PCA, T-SNE & ML

    Malware Detection & Interpretation – PCA, T-SNE & ML

    This post discusses the application of PCA, T-SNE, and supervised ML algorithms for malware detection using a benchmark dataset. Techniques such as Logistic Regression, SVC, KNN, and XGBoost are implemented, achieving high performance metrics. Results show potential for improving malware detection using ML while reducing false positives and enhancing cyber defense.

  • Retail Sales, Store Item Demand Time-Series Analysis/Forecasting: AutoEDA, FB Prophet, SARIMAX & Model Tuning

    Retail Sales, Store Item Demand Time-Series Analysis/Forecasting: AutoEDA, FB Prophet, SARIMAX & Model Tuning

    This study compares and evaluates various forecasting models to predict sales and demand for retail businesses. The focus is on Time Series Analysis (TSA) methods such as FB Prophet and SARIMAX. The final FB Prophet model yields MAE=4.252 and MAPE=0.168, while SARIMAX models’ best performing variant achieves MAE=6.285 and MAPE=0.213. The study emphasizes the importance…

  • H2O AutoML Malware Detection

    H2O AutoML Malware Detection

    This study explores AI-powered malware detection using the H2O AutoML algorithm for effective and rapid classification of PE files. The optimized Stacked Ensemble model achieved high precision, recall, and F1 score. The research validates the H2O AutoML workflow’s accurate malware identification and supports related R&D products and solutions in the field of information security.

  • Anatomy of the Robust 1D Kalman Filter

    Anatomy of the Robust 1D Kalman Filter

    The Kalman Filter (KF) is a powerful tool for tracking, navigation, and data prediction tasks. It is based on the assumption of linearity and Gaussian noise, enabling it to iteratively update predicted models. The article outlines a simplified implementation of KF using Python commands, with examples demonstrating its effectiveness in handling noisy measurements. It also…

  • Basic Python Programming

    Basic Python Programming

    This guide introduces basic concepts and features of the Python programming language. It covers a range of topics, including installation, variables, strings, lists, tuples, sets, dictionaries, loops, conditionals, functions, and modules. The comprehensive content provides valuable information for beginners seeking to learn Python for data science or general programming.

  • A Market-Neutral Strategy

    A Market-Neutral Strategy

    The work aims to solve the problem of Markowitz portfolio optimization for a one-year investment horizon through the pairs trading cointegrated strategy. Market-neutral trading strategies seek to generate returns independent of market swings to achieve a zero beta against its relevant market index. Statistical arbitrage (SA), pairs trading, and APO signals are analyzed. The study…

  • A Comprehensive Analysis of Best Trading Technical Indicators w/ TA-Lib – Tesla ’23

    A Comprehensive Analysis of Best Trading Technical Indicators w/ TA-Lib – Tesla ’23

    This study presents a comprehensive stock technical analysis guide for Tesla (TSLA) using the TA-Lib Python library. It explores the use of over 200 technical indicators, analyses historical data, and offers insight for both swing traders and long-term holders. The content includes detailed explanations and plots for various momentum, volume, volatility, and trend indicators, providing…

  • Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    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.

  • A Balanced Mix-and-Match Time Series Forecasting: ThymeBoost, Prophet, and AutoARIMA

    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…

  • Plotly Dash TA Stock Market App

    Plotly Dash TA Stock Market App

    The post explains how to deploy a Plotly Dash stock market app in Python with the dashboard of user-defined stock prices. This includes technical indicators like volume, MACD, and stochastic. The steps include selecting a stock ticker symbol (NVDA), retrieving stock data from yfinance API, adding Moving Averages, saving the stock chart in HTML form,…

  • NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    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…

  • Oracle Monte Carlo Stock Simulations

    Oracle Monte Carlo Stock Simulations

    Oracle Corporation’s significant developments in Generative AI have led to lucrative partnerships with Nvidia and Elon Musk’s xAI. Having secured contracts exceeding $4 billion for its Generation 2 Cloud designed for AI model training, Oracle’s earnings doubled in Q4 2023. Monte Carlo simulations align with Zacks Rank 3-Hold for ORCL, implying bullish potential with projected…

  • NVIDIA Rolling Volatility: GARCH & XGBoost

    NVIDIA Rolling Volatility: GARCH & XGBoost

    This post examines the prediction of NVIDIA stock volatility using two models: the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and the Extreme Gradient Boosting (XGBoost). Both models are compared in terms of MSE and MAPE. The post discovers that the machine learning-based XGBoost model outperforms the GARCH model in NVDA volatility forecasting, showing the effectiveness of…

  • An Implemented Streamlit Crop Prediction App

    An Implemented Streamlit Crop Prediction App

    Precision agriculture or smart farming: We implement the Streamlit crop prediction app. This is an ML-driven app that requires the trained model as input.

  • Blue-Chip Stock Portfolios for Quant Traders

    Blue-Chip Stock Portfolios for Quant Traders

    This post delves into optimizing blue-chip stock portfolios using Python fintech libraries for private DIY self-traders. It includes steps for examining trading signals, comparing stock returns, performing analyses, and implementing forecast models. The content covers AAPL trading signals, risk vs. ROI analysis, a 4-stock portfolio, Monte-Carlo predictions, SPY return/volatility, and SPY Prophet forecast. The examples…

  • Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Table of Contents PJME Data Let’s set the working directory YOURPATH and import the following key libraries Let’s read the input csv file in our working directory Let’s plot the time series Data Preparation Output: (113926, 1, 9) (113926,) (31439, 1, 9) (31439,) LSTM TSF Let’s plot the LSTM train/test val_loss history Output: MSE: 1811223.125…

  • Morocco Earthquake EDA

    Morocco Earthquake EDA

    Featured design via Canva. Clickable Table of Contents Basic Installations and Imports Let’s set the working directory YOURPATH Let’s install and import the following libraries Download Earthquake Input Data For this project, we’ll use a dataset that contains all seismic events over the last seven days, which have a magnitude of 1.0 or greater: Output:…

  • EUR/USD Forecast: Prophet vs JPM

    EUR/USD Forecast: Prophet vs JPM

    JP Morgan (JPM) analysis predicts the EUR/USD exchange rate to hold at 1.08 in December 2023, while ING forecasts suggest rates of $1.00 throughout 2023 and $1.02 in Q1 2024, rising to $1.10 by Q4 2024. Using the FB Prophet model, predictions show a hold at 1.08 +/- 0.07 in December 2023, aligning with JPM’s…

  • WA House Price Prediction: EDA-ML-HPO

    WA House Price Prediction: EDA-ML-HPO

    A predictive model of house sale prices in King County, Washington, was developed using multiple supervised machine learning (ML) regression models, including LinearRegression, SGDRegressor, RandomForestRegressor, XGBRegressor, and AdaBoostRegressor. The best-performing model, XGBRegressor, explained 90.6% of the price variance, with a RMSE of $18472.7. These results, valuable to local realtors, indicate houses with a waterfront are…