Predict number of rows using Machine Learnnig

Question

I have the bank data of around 4 years of different branches. I am trying to predict number of rows in daily and hourly level. I have issue_datetime (year, month, day, hour) as important features. I applied different regression techniques (linear, decision trees, random forest, xgb) using graph lab but could not get better accuracy. I was also thinking to set the threshold based on past data like taking the mean of counts in daily, monthly level after removing outliers and set that as a threshold. What is the best approach?

Because What is the best approach? cannot be answered meaningfully. And even if someone tries to, the answer would be highly speculative. All in all off-topic for stackoverflow. — cel
– cel, Commented Jan 20, 2017 at 8:18
I agree with @cel. But I think before trying to make predictions trying to get rid of your seasonality patterns may help improving your prediction. — Shobeir
– Shobeir, Commented Jan 20, 2017 at 8:29
I am open to suggestions also like is there any other way to achieve this task? — user1584253
– user1584253, Commented Jan 20, 2017 at 8:30
This question would be better suited on datascience.stackexchange.com . That being said: you are basically trying to model a behavior, and you will want to test different models. I have once solved a similar question (varying intensities over time) with Fourier analysis (and in that cased it worked very well). Fourier presuposses that the observed intensities are caused by stacked periodical events. — S van Balen
– S van Balen, Commented Jan 20, 2017 at 9:55
Get a book on time series prediction. We can't put all this information in an answer here. — Has QUIT--Anony-Mousse
– Has QUIT--Anony-Mousse, Commented Jan 21, 2017 at 8:59

Him · Accepted Answer · 2017-01-20 09:57:49Z

1

Since you have 1d time series data, it should be relatively easy to graph your data and look for interesting patterns.

Once you establish that there are some non-stationary aspects to your data, the class of models you are probably wanting to check out first are auto-regressive models, possibly with seasonal additions. ARIMA models are pretty standard for time-series data. http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA-model-in-python/

answered Jan 20, 2017 at 9:57

Him

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