In this article, I will show you a time series forecasting method I haven’t seen documented elsewhere. I doubt it is a new method, but since I haven’t seen a great article on it, here it is.

In this article, I will show you a time series forecasting method I haven’t seen documented elsewhere. I doubt it is a new method, but since I haven’t seen a great article on it, here it is.

**The Dataset**

The data I used for this project is the data from the Global Energy Forecasting competition, put on by my hometown university, UNC Charlotte. You can find more about it here: http://www.drhongtao.com/gefcom/2017

What you need to know is the data is various readings from an energy grid. Our target is to forecast real-time energy demand for the grid using these data points. The data points also include dew point and dry bulb temperature, since air conditioning is a huge driver of energy consumption.

Our target variable is RTDemand: Real Time energy demand for the energy grid we are working with. The data has clear daily cycles. Here are three days of our data:

Hourly for 3 days

In the middle of the night when the sun is down and everyone is asleep, our power consumption reaches a minimum. We wake up in the morning, head off to work, and our power consumption reaches its maximum as the sun reaches peak intensity. I think the daily dips correspond to commuting times.

If we zoom out a little more, we can see clear auto-correlation and trends in days, just as you see in weather. Here’s about 3 weeks of data:

3 Weeks of the target variable

energy machine-learning python convolutional-network time-series-analysis

The “sklearn” for time series forecasting, classification, and regression. Existing tools are not well-suited to time series tasks and do not easily integrate together. Methods in the scikit-learn package assume that data is structured in a tabular format and each column is i.i.d. — assumptions that do not hold for time series data.

In this article, we will be discussing an algorithm that helps us analyze past trends and lets us focus on what is to unfold next so this algorithm is time series forecasting. In this analysis, you have one variable -TIME. A time series is a set of observations taken at a specified time usually equal in intervals. It is used to predict future value based on previously observed data points.

Learn Machine Learning with Python using neural networks with this machine learning beginners course. In this tutorial we will look at taking an existing sol...

Bonus intro to keywords like seasonality, trend, autocorrelation, and much more.

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.