@prabhat7298 wrote:
I need some help on AV practice problem of time series analysis.Dataset has hourly data which has multiple periodicity. Actually the given series is somewhat stationary but when we apply one difference to reduce the trend the statistics of dickey fuller test got improved( p value comes out to be exactly 0). So, I tried to apply ARIMA model to the one difference time series but the results were awful…I also tried to tweak the Parameter model with the help of ACF and PACF but failed!Moreover the ACF and PACF are not showing the seasonality that I can see in raw data. When I tried to fit the model directly on the given series the results were pretty good! Initially I tried to get the ARIMA model parametrers( p,q,d) with the help of ACF and PACF but then I think of an automated pipeline to get the best parameters. In the automated pipeline I used walk forward validation to get the MSE of different combinations of ARIMA model parameters but this process is taking a lot of time and couldn’t complete I think because of large dataset and the repetive model framing nature of walk forward validation. So, I tried to reduce the test dataset to a minimal value of last 3 days. The MSE of many model comes out to be close enough making it difficult for me to decide which ones should I choose! Please can anyone help me out in explaining why the difference time series give bad results and what should be a good technique to validate the model. Moreover, I’m thinking that since the time series has a weekly trend which repeats itself after every week…maybe we can take a difference of 24*7 in time series to eliminate this seasonality or we may make some own features like 1. Day of the month
2. Hour of the day
3. Day of the week
4. Ordinal date (Number of days from January 1 of year 1) and then use some other models like GBM or random forest. Please Guide me …I’m totally confused
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