Generic ARIMA analysis for time series implemented from https://towardsdatascience.com/time-series-in-python-exponential-smoothing-and-arima-processes-2c67f2a52788
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Kumar, Prashant. "Forecasting Cloud Resource Utilization Using Time Series Methods." (2018). http://www.diva-portal.se/smash/get/diva2:1273037/FULLTEXT01.pdf (ARIMA + ES) > ARIMA FFNN > (ARIMA + ES)
- Good description of data preparation. We can adapt our data to use the same configuration without too many problems Just uses the CPU measurements
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Paul Newbold and C W. J. Granger. “Experience with Forecasting Univariate Time Series and the Combination of Forecasts”
- This paper suggested that a combination of individual forecasting models performs better than any individual forecasting.
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Islam, S., Keung, J., Lee, K., & Liu, A. (2012). Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 28(1), 155-162.
- “... Individual samples may not be a representative of the true resource utilization level… ”
- “This kind of data tends to have a trend” - Kumar, Prashant. "Forecasting Cloud Resource Utilization Using Time Series Methods." (2018).
- The same data gaps for CPU and Memory
- The CPU and Memory are High correlated
- It's a bad idea to use CPU and Memory together as a hyperparameters, but it’s a good idea to test them individually to check the true representativeness
- The data gaps are filled using interpolation techniques
- A standard for all the files considering a hourly frequence
- A trend (upward or downwards movement of the curve on the long term)
- A seasonal component
- Residuals
About ARIMA:
- ARIMA models should be used on stationary data only.
- To obtain the best performance of model, we need to make the data stationary
- De-trending
- Seasonal adjustment
- Transformation
- Smoothing
- Time series with trends, or with seasonality, are not stationary
- Smoothing methods work as weighted averages. Forecasts are weighted averages of past observations.
- Simple Exponential Smoothing
- Few data points, Irregular data, No seasonality or trend.
- Holt’s Linear Smoothing
- Trend in data, No seasonality.
- Holt’s Damped Trend
- Data has a trend
- Exponential smoothing (ES)
- One of most flexible methods (related to time series patterns)