I have data from a call center morning 8 am to evening 8 pm with half an hour intervals. I am trying to perform time series forecasting to predict expected number of calls during the same time frame in the coming days. I have tried using ARIMA and exponential smoothing but they haven't given me any good results. I am not sure how to bring in seasonality into picture as its only 8 am to 8 pm data. But there are almost no calls on the weekends. Is there any other algorithm/technique which I should be trying ? Would really appreciate your help. Thanks I am attaching the screen shot of the data based on time series below. 
Solved – Time Series Forecasting – Hourly Data
forecastingmultiple-seasonalitiestime series
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Best Answer
TBATS from the forecast package in R should be useful for this, it is designed for handling multiple periodicities. Also, the Facebook Prophet forecast API can handle combinations of hourly and daily periodicities. I don't know whether it can handle half-hour periodicities or not yet (I know there were plans to do so, whether they implemented it yet or not I don't know).