I am working on multiple time series consisting of various sensor outputs. I am having data for more than 3 months duration. I am supposed to find p,d,q orders and coefficient values using the latest 200 values for the first time; and later update the model coefficients (while keeping the model order fixed as found from the 200 values) using the latest 100 values, at every 500 data points duration.
auto.arima (R 3.3.2) for finding the orders and coefficients for the first time (from 200 latest values) and then use
Arima to update coefficients after every 500 values (from the latest 100 values), keeping the orders the same as found by
I am supposed to forecast one-step ahead and find residual at every new coming streaming sensor value.
The time series
frequency is either 10 readings/minute, 1 reading/minute or 1 reading/10 minutes. As I have to consider the latest 200 values to decide orders, my time series data is either of these three cases:
- separated by seconds and spread over 20 minutes;
- separated by minutes and spread over 3 hrs and 20 minutes;
- separated by 10 minutes and spread over 33 hrs and 33 minutes.
Also, for some instances I get
- 5, 6, 7, 9, 11 readings in place of 10 readings/minutes,
- 2 readings in place of 1 reading/minute and
- 2 readings/10 minutes or 1 reading/11 minutes in place of 1 readings/10 minutes.
Provided this, my questions are:
- What should be the
frequencyparameter for the
tsfunction for the given 3 types of time series?
- Is it feasible to predict seasonality from 200 past values, or, we can avoid considering seasonality, as model parameters are getting updated at every 500 points?
- What will be the "season' for these three cases? Can I consider season as an hour?