Predictive algorithm for forecasting the mexican stock exchange. Machine Learning approach to forecast price and Indicator behaviours of MACD, Bollinger and SuperTrend strategy

Exponential Smoothing, SARIMA, Facebook Prophet

📈 My solution to Kaggle's "Web Traffic Time Series Forecasting" competition, which uses Facebook's Prophet library to make predictions

Time series forecasting is a technique for the prediction of events through a sequence of time. Time-series forecasting decomposes the historical data into the baseline, trend, and seasonality. When a forecasting model doesn’t run as planned, we want to tune the parameters of the method with regards to the specific problem at hand. Tuning these …

Predicting stocks using Facebook prophet. for some big tech companies (TSLA, GOOG ,MSFT, FB, AAPL, NVDA, PYPL, ADBE, NFLX )

Kaggle - Time-series Forecasting the optimal number of agents for a Contact Center: Facebook Prophet, InfluxDB Holt-Winter

Data Science web application for automated forecasting of univariate time-series data using Facebook Prophet on Python-Streamlit.

Time series analysis showing trend, seasonality, and periodicity decomposition; and forecasting using Facebook Prophet. The analysis makes extensive use of indexing data tools and of the Pandas and Datetime libraries.

Tesseract: Stock Market Forecasting 📈 Engine with JavaFX, Alpaca Trading and Facebook Prophet 💸 to analyze, forecast and trade stocks.

PREDICTING CRIME RATE IN CHICAGO USING FACEBOOK PROPHET

Time Series Using ARIMA and Facebook Prophet

This repository is created just for learning purpose, here we are going to implement various supervised machine algorithms based on the data to predict the target variable.

Honours Dissertation Resources

Preciting avocado prices using Facebook Prophet

Prediction of future sales with fbprophet

Utilized facebook prophet to perform forecasting on datasets that consist sales data from 1115 stores. Our predictive model attempts at forecasting future sales based on historical data while taking into account seasonality effects, demand, holidays, promotions, and competition.

Blent oil price forcast using Facebook Prophet

Predicting The Future Using Facebook's Unsupervised Learning Machine Learning Procedure Prophet

This repository covers essential techniques for time series analysis and forecasting. It covers data manipulation and visualization using Numpy and Pandas, time series analysis with Statsmodels, ARIMA models, deep learning methods like RNNs, LSTM, GRU, etc. and Facebook's Prophet library.