Need urgent help regarding this.
I am currently working on a project involving multiple time series forecasting using R. For the forecasting process, I have monthly data available from 2019 up to 2023.
Currently, I have generated baseline forecasts using R, specifically using ARIMA and ETS models. I have attached figures depicting the behavior of one of the time series along with its corresponding forecasted values based on the applied mathematical models.
However, I am facing an issue where most of the forecasted data does not seem to capture the seasonality well. The forecasts appear to be linear rather than capturing the expected seasonal patterns.
However, I am facing an issue where most of the forecasted data does not seem to capture the seasonality well. The forecasts appear to be linear rather than capturing the expected seasonal patterns.
Code:
t_data.fit = t_data %>%
model(arima=ARIMA(Demand)
)
If you are using the Autoarima function in R, it is likely that you might get linear forecast results. I recommend you check data properties (autocorrelation, unit root) and required differencing first. Then do a manual estimation of p,d,q.
You must use a SARIMA model to capture the seasonal effects. This is well explained in Rob J Hyndman and George Athanasopoulos's book Forecasting: Principles and Practice (3rd ed), Otexts. An online version is available at
https://otexts.com/fpp3/
The examples in this book are in R. Section 9.9 deals with seasonal ARIMA (SARIMA) models.
There are many reasons why your forecasts may appear to be linear rather than capturing the expected seasonal patterns. One possible reason is that the model you are using is not appropriate for the data you are working with. Another possible reason is that you have not included all of the relevant variables in your model.
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