I have a full year of 2017 daily sales data and am looking forward to predicting the daily sales for next month. It has strong seasonality of 7, so what I did is to use Holt-Winters to calculate the **level**, **trend** and **seasonal** components from the 8th data points, fill it down in Excel till the last day of 2017, and start predicting from Jan/01/2018. I have the following questions:

Is it necessary to use all the data I have? So far I have read a few tutorials about Holt-Winters and it seems that they don't use a lot of data (e.g. if they want to predict for the next 4 quarters they generally use past data of 3-4 years)

Is it OK to predict for a whole month when the seasonality is 7? When predicting for the next month, I'm using the seasonal components 30 days ago, but since the data has a period of 7, should I only predict for the next

**WEEK**with the seasonal components of last**WEEK**and then calculate the new seasonal components of**THIS WEEK**based on the predicted data, and go on like this? I'm pretty confused about this.

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#### Best Answer

Well, more data is nearly always better than less, so the answer to Q1 is "yes". With only 365 data points you hardly have a big data computational problem, so why wouldn't you use it? More data gets you different (and almost certainly better) estimates of both the trend and the seasonal elements in the data.

However, you have a bigger problem which is that while you have enough data to pick up the frequency=7 weekly seasonality, just as (probably more) important will be an annual sales cycle. The sales in January 2017 will be very useful in forecasting sales for January 2018 – probably more useful than the recent December 2017 sales are. Unfortunately, with only one year of data, you aren't really in a position to identify the seasonal January effect from randomness, without making some additional assumptions. You need a minimum of two full periods (in this case, two years) to seasonally decompose a time series.

So you need to use some arbitrary method – there just isn't enough data to separate out into trend, the two levels of seasonality, and randomness. If it's for a stable firm you could just use last January's sales as the guess for this January's; if it's going through substantial growth or decline you're probably best off continuing with Holt-Winters which will at least pick up the trend, but that's very much a *relatively* best off, not a particularly strong endorsement!

Re your question 2, there's nothing stopping you forecasting any arbitrary number of days forward. Think of this thought experiment – you forecast forward 10 weeks ie 70 observations, and then just delete the final 40, 50, or 60 leaving you with 30, 20 or 10 forecast days.

You'll find it easier if you do this forecasting in a statistical package (eg open source R and the `forecast`

R package which looks after it all for you with `forecast(ets(mydata), h = 30)`

to do a 30 day forecast) which has it built in rather than trying to create the algorithm by hand in Excel. Of course, you then get a learning curve with getting your data into R but I'd say it would be worth it.

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