# ML Times Series Modeling

The Importance of Creating a Model with Stationary Data. Through out my data science journey I have learned so many different modeling techniques

Through out my data science journey I have learned so many different modeling techniques, but I just had not found my niche yet, and I had been patiently waiting for the right machine learning model to come along and sweep me off my feet. I had been working in the business, finance, and fintech space for the past few years and my love for business forecasting and predictions to help maximize shareholder’s wealth had to stay center staged.

Then one day, I discovered the machine learning Time Series Model! I was hooked on day one. The fact that I can create a model to test/train on past performances to help forecast the future is very interesting to me. How one can incorporate ML, Deep Learning, Neural Networks, and Auto Regression modeling to solve business problems is absolutely genius!

So the biggest question is, Exactly what is Time Series Modeling? In simple terms, it is the use of a model to predict future values based on previously observed values. You can use Time Series in the following industries:

1. Retail Industry

2. Energy Industry

3. Government

4. Financial Organization

5. Agriculture

## The Time Series Model ARIMA

One of the most popular models to implement is the ARIMA model, which stands for Autoregressive Integrated Moving Average. ARIMA models aim to describe the autocorrelations in the data and incorporates the following concepts:

AR = Autoregression

I = Integration

MA = Moving Average

## Analysis, Price Modeling and Prediction: AirBnB Data for Seattle.

Analysis, Price Modeling and Prediction: AirBnB Data for Seattle. A detailed overview of AirBnB’s Seattle data analysis using Data Engineering & Machine Learning techniques.

## Series Data Analysis Methods and Forecasting Models in Python

The following code divides the earth surface temperature trend time series into training and testing sub-series first and then uses the training data to train an ARIMA model.

## Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

## Tableau Data Analysis Tips and Tricks

Tableau Data Analysis Tips and Tricks. Master the one of the most powerful data analytics tool with some handy shortcut and tricks.

## What Is Data Analysis?

DISCLAIMER: absolutely subjective point of view, for the official definition check out vocabularies or Wikipedia. And come on, you wouldn’t read an entire article just to get the definition.