Forecasting stationary data that has no trend/seasonality

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In summary, the conversation discusses using a random variable with 53 values to forecast the upcoming 5 days. The variable is the number of warranty claims received each day and appears to have no trend or seasonality. The speaker is attempting to model the data using an ARIMA model, and suggests that AR(1), MA(1), or ARIMA(1,0,1) would be the best options based on the autocorrelation and ACF/PACF plots. However, there is a question about whether ARIMA is the best method for forecasting this type of data.
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Deimantas
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Homework Statement



We've got a random variable that appears to have no trend/seasonality, is stationary, and we want to forecast it.
The variable is number of warranty claims received each day, 53 days, so we've got 53 values, and we want to forecast the values of the upcoming 5 days.

2. The attempt at a solution

I'm trying to model the data using ARIMA model.
1.png


Judging from the autocorrelation plots, the data is stationary, so no differencing should be done. Judging from the ACF and PACF plots, our best bet would be AR(1), MA(1) or ARIMA(1,0,1). All yield similar results:
results1.png
results2.png
forecast.png


Is there no better way to forecast this variable? ARIMA does not seem like a good forecasting option in this case. The data has no apparent trends/seasonalities and is stationary. What method would be best to forecast such data?
 
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  • #2
ARIMA is a very powerful and general method for modeling time series. I don't know what you might try that would be better.
 

1. What is the definition of "stationary data"?

"Stationary data" refers to a time series dataset where the mean and variance of the data do not change over time. This means that the data does not have any trend or seasonal patterns.

2. Why is it important to forecast stationary data?

Forecasting stationary data is important because it allows us to make predictions about future values of the data. This can be useful in a variety of fields such as economics, finance, and weather forecasting.

3. How do you identify if data is stationary?

There are several methods for identifying stationary data, including visual inspection of a time series plot, statistical tests such as the Dickey-Fuller test, and using techniques such as differencing to remove trend and seasonality.

4. What are some common techniques for forecasting stationary data?

Some common techniques for forecasting stationary data include moving average models, autoregressive models, and ARIMA (autoregressive integrated moving average) models. These methods use past data to make predictions about future values.

5. Can we use the same forecasting techniques for non-stationary data?

No, the forecasting techniques used for stationary data may not work for non-stationary data. For non-stationary data, additional steps such as differencing or seasonal adjustment may be necessary to make the data stationary before applying forecasting techniques.

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