Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
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Updated
Dec 11, 2018 - Jupyter Notebook
Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.
Projetos de modelagem e previsão de séries temporal em linguagem Python e linguagem R. Usarei vários modelos de bibliotecas e pacotes usados para tratamento, modelagem e previsão de séries temporais. Falarei um pouco sobre cada uma delas, gerarei a validação e as previsões e, por fim, realizarei a avaliação com a métricas pertinentes.
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
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Recently inflation is a popular topic in Poland and is highest since 2001. Experts presume inflation in Poland should continue to rise, and by the end of 2021 it will be close to 8%. This notebook aims to develop a forecasting model for time series using Python.
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In 2021, a precise forecast of Iran Post's 2021-2022 income was achieved using ARIMA, with only a 1.5\% error. This approach was subsequently extended to estimate the income and traffic for 2022-2023.
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Two Jupyter Notebooks written in Python, treating of time series analysis with ARIMA and its seasonal counterpart.
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