Lightning ⚡️ fast forecasting with statistical and econometric models.

NeuralProphet: A simple forecasting package

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Price calculator/predictor for Turnip prices

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

Modeltime unlocks time series forecast models and machine learning in one framework

（陆续更新）重新整理过的基于机器学习的股票价格预测算法，里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测，包含：LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM、随机森林等

Streamlit app to train, evaluate and optimize a Prophet forecasting model.

A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

Amazon SageMaker Local Mode Examples

CryptoCurrency prediction using machine learning and deep learning

Book and material for the course "Time series analysis with Python" (STA-2003)

A multiverse of Prophet models for timeseries

光伏短期功率预测大赛 代码

Shiny App that offers an interactive interface to explore the main functions of the [prophet Package](https://cran.r-project.org/package=prophet)

A simple neural net implementation.

Time Series Forecasting for the M5 Competition

Time Series Analysis project with Prophet.

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

Shiny app for Price Optimization using prophet and lme4 libraries for R.

Compendio de conocimiento sobre series temporales, para la predicción de series temporales con todos los métodos tratados en nuestro laboratorio DICITS.

Arima, Sarima, LSTM, Prophet, DeepAR, Kats, Granger-causality, Autots

Time-series demand forecasting is constructed by using LSTM, GRU, LSTM with seq2seq architecture, and prophet models.

Python notebooks for demonstrating various ideas, APIs, libraries.

Applying Facebook's prophet on Google Analytics data

dave smith instruments prophet rev2 presets book

Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest Neighbor, Support Vector Machine, Decision Tree, and Random Forest to identify which algorithm gives better results. Used Neural Networks such as Auto ARIMA, Prophet(Time-Series), and LSTM(Long Term-Short Memory) then compare make Inferences about the model.

Time Series forecasting using Seasonal ARIMA & Prophet. Applied statistical tests like Augmented Dickey–Fuller test to check stationary of series. Checked ACF ,PACF plots to identify Moving average and Auto-regressive order of series. Transformed series to make it stationary.

supercollider class to talk to prophet rev2 hardware synthesizer

A deep exploration of AI-driven foresight, how predictive models evolve into strategic collaborators. This repository presents Forecasting the Future of Forecasting, a professional essay on reflexive intelligence, human–AI collaboration, and the design of adaptive, explainable forecasting systems.