Forecasting data of export Wh-with irregularly spaced time series

I am new to R. I am analyzing export Wh, data for my project.
Original csv file (org_data.csv) that I m analyzing can be found in this link:
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In the original csv file, there are 1,584,823 total records, with 157 meters.
Recorded from 1-Oct-2015 00:00:00 to 31-Mar-2016 23:59:59

In the csv file, there are three columns: local minutes, dataID, meter_value.

  • Local minutes is formatted with “yyyy-mm-dd hh:mm:ss-UTC
    time offset”.
  • dataID represents the ID number for each 157 meter,
  • meter_value represents the export Wh

Quick view of original csv file, as below:

2015-10-01 00:00:10-05,739,88858
2015-10-01 00:00:13-05,8890,197164
2015-10-01 00:00:20-05,6910,179118
2015-10-01 00:00:22-05,3635,151318
2015-10-01 00:00:22-05,1507,390354
2015-10-01 00:00:29-05,5810,97506
2015-10-01 00:01:18-05,6910,179118
2015-10-01 00:01:34-05,6910,179118
2015-10-01 00:01:38-05,5810,97506
2015-10-01 00:01:43-05,4352,218216
2015-10-01 00:01:46-05,484,99298
2015-10-01 00:01:56-05,1718,161076
2015-10-01 00:01:58-05,1714,147048

I have done plotting the existing data of all the 157 meters using facelet

But I stuck in writing algorithm for predicting export Wh of all 157meters (From 01 Apr 2016 00:00:00 to 31Dec2016 11:59:59).

May I know how should I develop forecasting model for all 157 meters, with this UTC time offset?

As i have to submit this prediction algorithm by 20-Oct-2018. I am grateful if you guys help me.

It sounds like this might be a homework question. If it is, please see our homework policy.

If you've written some code and are having a particular problem, please add a minimal reprex (reproducible example) to your post. This will ensure we're all looking at the same data and code. A guide for creating a reprex can be found here.

Hi @may! I'm afraid this is rather an enormous question. If you've already gotten started writing code based on any references you may have read (books, websites), it would help if you could provide the code you have and links to the references you've consulted.

If you haven't gotten started yet, I think the best that anybody's going to be able to do is to give you general pointers at references where you can start learning about forecasting. There's not one way to do this sort of thing — your statistical judgement and your knowledge of the larger project and goals (where do the data come from? what conditions do you expect to be different over the new time period? what is the forecast going to be used for?) will always play a big role in choosing and implementing your method. You and the people you directly work with are best positioned to provide these crucial elements.

All that said, if you are just getting started, this is a go-to reference: Forecasting: Principles and Practice (2nd ed)