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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.

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

A multiverse of Prophet models for timeseries

光伏短期功率预测大赛 代码

Time Series Forecasting for the M5 Competition

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

Applying Facebook's prophet on Google Analytics data

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.

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.

CentOS based Docker container for Time Series Analysis and Modeling.