I'm relatively new to time series forecasting. I've been assigned with the task of forecasting operation time of an industrial equipment based on a daily data (3 years of daily data).
The prediction is desired for at least 6 months in future .I've investigated time series forecasting domain for the past few weeks to come up with possible models for my forecasting problem. After reading out several related questions in this helpful community, I have tried my hands with
auto-arima package of python.
What have I tried so far?:
- Aggregated the daily data into a weekly sum
- Understood the seasonal decomposition of the data using
statsmodellibrary and there is a clear seasonality in data
- Split the data into train and test set
- Fitted an
auto-arima seasonalmodel on train set and generated an out of sample forecast for the length of test period. Predictors such as holiday week, week of the year have been given as input.
- Compared the actual test data and arima forecasted data with
The MAE of raw weekly summed data is higher than that of rolling window averaged weekly summed (window=8) input train data. Here is the result of my model forecast on rolling averaged data:
Fit ARIMA: order=(2, 0, 2) seasonal_order=(1, 1, 0, 52); AIC=558.923, BIC=585.271, Fit time=44.283 seconds
I have a question with regards to model development and testing of time series forecasting:
- Here is how my raw data look:
Is it a common practice to apply rolling mean on the raw data before fitting an
arima-seasonalmodel? (I understand that some valuable information will be lost by averaging. But what if I can trade off some valuable information for a reasonable model?). Fitting on averaged data resulted in a better out of sample forecast compared to fitting on a raw data. I am unable to find information on this practice with my limited internet search on this topic.
Any reference to documentation for fitting on a noisy data is appreciated. I am ready to invest more time to understand the time series modelling thoroughly. I know I have barely scratched the surface of time series modelling but what puzzles me the most is
how good the forecast is for weekly summed rolling window averaged (window =8) out of sample forecast.
I shall email the data if necessary.