Tag: prophet

  • 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…

  • Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    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…

  • 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…

  • 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…

  • 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…

  • BTC-USD Freefall vs FB/Meta Prophet 2022-23 Predictions

    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…

  • Stock Forecasting with FBProphet

    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.