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I have plotted the frequency of items sold through time, trying to determine the trends by moving average. I considered a 5-days window. I would like to know if this approach makes sense and how I could interpret the results. It's my first time with time-series and moving average (I have no a scientific background at all).
I hope you can help me.
Yes it makes sense, a moving average makes the curve "smoother" in the sense that it's less sensitive to short variations. This usually makes it easier to observe the general tendency.
You could also try different time periods for the average, e.g. 10 days or 15 days.
It looks to me like there's a moderate increase trend in your data, but the variations are important and the time window is short, so it's too early to be sure. You could apply linear regression to confirm the increase.
Moving averages will give you a smoother time series so that a trend is easier to see by eye. This approach makes sense when you’re exploring the data.
The next step is to try to comment on where the time series will go next. Based on the tags you have chosen for your question, you are comfortable writing python. You might consider Facebook’s open source time series forecasting package. This will allow you to analyse the seasonality of your sales (e.g. how does the day of the week or the month of the year have an effect?) and allow you to understand what the trend is doing when the seasonal effects are removed. There tutorial for the python API is here.
