ML Times Series Modeling

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

forecasting-models data analysis

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