I am pretty new to ML and completely new to creating my own models. I have went through tensorflows time-series forecasting tutorial and other LSTM time series examples on how to predict with multi-variate inputs. After trying multiple examples I think I realized that this is not what I want to achieve.

My problem involves a dataset that is in hourly intervals and includes 4 different variables with the possibility of more in the future. One column being the datetime. I want to train a model with data that can range from one month to many years. I will then create an input set that involves a few of the variables included during training with at least one of them missing which is what I want it to predict.

For example if I had 2 years worth of solar panel data and weather conditions from Jan-12-2016 to Jan-24-2018 I would like to train the model on that then have it predict the solar panel data from May-05-2021 to May-09-2021 given the weather conditions during that date range. So in my case I am not necessarily forecasting but using existing data to point at a certain day of any year given the conditions of that day at each hour. This would mean I should be able to go back in time as well.

Is this possible to achieve using tensorflow and If so are there any resources or tutorials I should be looking at?

CodePudding user response：

See Best practice for encoding datetime in machine learning.

Relevant variables for solar power seem to be hour-of-day and day-of-year. (I don't think separating into month and day-of-month is useful, as they are part of the same natural cycle; if you had data over spending habits of people who get paid monthly, then it would make sense to model the month cycle, but Sun doesn't do anything particular monthly.) Divide them by hours-in-day and days-in-year respectively to get them to [0,1), multiply by 2*PI to get to a circle, and create a sine column and a cosine column for each of them. This gives you four features that capture the cyclic nature of day and year.

CyclicalFeatures discussed in the link is a nice convenience, but you should still pre-process your day-of-year into a [0,1) interval manually, as otherwise you can't get it to handle leap years correctly - some years days-in-year are 365 and some years 366, and CyclicalFeatures only accepts a single max value per column.