Neste tutorial iremos iniciar o Econowallet, essa aplicação que vai contar com uma API para controle financeiro de suas despesas e investimentos, se você não viu o post passado (onde explico mais sobre meu objetivo com esse projeto) clique aqui.

Tópicos que serão abordados nesse post:

• Setup Inicial: main.py e config.py
• Rotas async

Setup

Como todo projeto python, é uma boa prática que você crie um ambiente virtual isolando as dependências do projeto, isso evita que você possa ter conflito entre diferentes libs de outros projetos que esteja trabalhando. Para este fiom temos várias opções:

Motivação

Atualmente tenho tido bastante dor de cabeça em voltar a utilizar o Excel para fazer uma planilha de controle de gastos, apesar da simplicidade do software, constantemente meus registros de compras feitas ou alguma outra movimentação financeira ficam uma completa bagunça. O que pode ser justificado pela minha falta de destreza com a ferramenta, mas fato é que a experiência estava deixando a desejar e consequentemente acabei perdendo a disciplina de organizar meus gastos, o que não recomendo a ninguém.

Demand Forecasting of Brazilian Commodities

Soybean, Corn, Sugar, Soybean Meal, Soybean Oil and Wheat (left to right).

Demand Forecasting is a technique for estimation of probable demand for a product or services. It is based on the analysis of past demand for that product or service in the present market condition. Demand forecasting should be done on a scientific basis and facts and events related to forecasting should be considered.

Correlation analysis is a key task when you’re exploring any dataset. The principal objective is to find linear relationships between features that can help to understanding the big picture.

Probably, the best way to see correlations between variables is to use scatterplots, but in most of time you’re working with a high dimensional dataset with a high number of variables, in these situations you have two major problems:

Introduction

This is the first of two articles that we’ll talk about two different approaches to perform hypotheses tests, covering the classical and computational methodologies. In the end I’ll show you one R package (Infer) capable to execute any of these methods in an easy, flexible, and less error-prone way.

In the second article, we’ll go deeper in a hands-on experiment using the Infer package, if you already know the package and want to see more code than text, click here.

What is it correlation analysis?

The concept of correlation is the same used in non-time series data: identify and quantify the relationship between two variables. Due to the continuous and chronologically ordered nature of time series data, there is a likelihood that there will be some degree of correlation between the series observations.

Measuring and analyzing the correlation between two variables, in the context of time series analysis, can be understood by two different aspects: