Lightning ⚡️ fast forecasting with statistical and econometric models.

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

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

Hierarchical Time Series Forecasting with a familiar API

The set of functions used for time series analysis and in forecasting.

Time Series Analysis with Python Cookbook, Second Edition - Published by Packt

Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models

Real-time time series prediction library with standalone server

Forecasting Monthly Sales of French Champagne - Perrin Freres

Time Series Forecasting Methods — A collection of Python implementations for essential time series forecasting techniques, including Simple, Double, Triple Exponential Smoothing, and Moving Averages.

A learning tool to demonstrate the process of financial forecasting, budgeting, and analysis.

Exponential Smoothing, SARIMA, Facebook Prophet

No description provided.

Forecasting Time Series with Moving Average and Exponential Smoothing

Borealis AI mentored water consumption prediction machine learning web application!

Theta methods for time series forcasting

Brazilian PIB (GDP) time series analysis.

Holt-Winters exponential smoothing implemented in Go.

The Korea National Oil Corporation was interested in purchasing shale gas wells from the United States and wanted to predict their production to select wells that maximize profit.

Identified the most appropriate Time-Series method to forecast drought in African countries, acting as a critical early warning for drought managements

Time Series forecasting model for predicting the unit’s movement of the inventory in the warehouses and stores in order to do capacity planning and prepare for peak volume at the granularity level of week/channel/location.

Forecasting the monthly Sales of Shampoo for next 6 months using various models Linear Regression, Naive Approach, Simple Average, Moving Average, Simple Exponential Smoothing,Double Exponential Smoothing, Triple Exponential Smoothing ARIMA and SARIMA Models in Python.

This project is to build Forecasting Models on Time Series data of monthly sales of Rose and Sparkling wines for a certain Wine Estate for the next 12 months.

Real-time smoothing/de-noising via exponential moving average and variable smoothing factor

Time Series Analysis and Forecast System

Time Series Forecasting Experiments A collection of hands-on experiments with time series data, featuring models like ARIMA, LSTM, and Prophet. From data preprocessing to forecasting, explore real-world applications like stock predictions and weather forecasting. Continuously updated with new techniques and models for better performance.

Predicting Cryptocurrency Prices with Machine Learning - Time Series Forecasting

P-140 Air Quality forecasting(CO2 emissions) Business Objective: To forecast Co2 levels for an organization so that the organization can follow government norms with respect to Co2 emission levels. Data Set Details: Time parameter and levels of Co2 emission