Time Series Analysis and Forecasting in Python

The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model. It also contains the implementation and analysis to time series anomaly detection using brutlag algorithm.

A Repo of Time-series analysis techniques. Holt-Winter methods, ACF/PACF, MA, AR, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA, RNN Keras, Facebook- Prophet etc.

Projet de prédiction d'électricité en France à partir de données réelles. Manipulation de données, modélisation de type régression linéaire, ainsi que différentes modélisations de séries temporelles (Holt-Winters, SARIMA).

Forecast the Airlines Passengers. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

Build models for forecasting Airline passenger traffic by utilizing several algorithms for time series analysis.

Keeping Inventory of spare in various service centre to the market demand is always a challenge as most service centres spends significant amount in spare parts inventory costs. In spite of this, availability of spare parts is been one of the problem areas.

A time-series forecasting model which forecasts CO2 emission levels based on available past data.

Analysis of different Forecasting techniques on a time series dataset to forecast the number of tourists in Australia in R

Implementation of various Time Series Methods 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.

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

Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

Program Exercises in R Language from book: "Forecasting, Time Series and Regression: An Applied Approach" / Ejercicios resueltos en R del libro "Pronosticos, Series de tiempo y Regresión: Un enfoque práctico" de Bruce L. Bowerman, Richard T. O´Connell, Anne B. Koehler, ISBN: 9789706866066 , Cuarta edición, Editorial: Thomson Año 2007

Business Problem: Oil price may fluctuate time to time based on more factors technical economical and natural as well as political so the forecasting may not be influenced by these some unexpected scenarios like Geopolitical issues (e.g.: War and Oil price Cap).

Trabajo Presentado en el Máster de Big Data, Data Science e IA del tema de Series Temporales

İBB'nin İkitelli'de bulunan güneş enerjisi panellerinin gelecek zamanda üretecekleri toplam enerjinin tahmininin yapılmasına ilişkin oluşturulmuş repository.

Neuro Retail and Optimisation System

Using MS Excel and R, accurately forecasted total core deposit data from a Richmond Bank. The Holt’s Linear Exponential Smoothing had the overall lowest “Quick and Dirty” MAPE (1.2%), the lowest overall Maximum MAPE (3.49%), and consistently more accurate projections for each of the forecast horizons. Overall, the Unaided, Holts Linear Exponenti…