Time Series Forecasting Holt Winters in R and Python

We have data on product sales at a monthly level of aggregation, with up to 5 years worth of data (varies by product) in a single csv file. Some forecasts are strongly seasonal, others a linear trend and others are fairly uniform. We require a robust monthly forecast for each product for each of the next 12 months. There are about 650 products and a total of about 15500 records. Note data has missing records where we sold zero, or where products were introduced or ceased part way through (treat these as zero). We want a batch program written in something like R or python that will produce sensible forecasts without intervention. There will be little human intervention. We would like a Holt Winters model with some statistical testing of parameters so that we adopt seasonality, trend if we find it, not otherwise. I believe there are libraries in R that can do this. I can look at re-formatting data if required. The output format is more fixed. We would also like a measure of forecast error (standard deviation) along with central forecast. I will post the actual data file to pre-qualified freelancers. We would also like separate program that can output a file with weekly values from the monthly file (a simple subdivision based on the number of mondays in each month - equal weekly value to be the same within a given month.

Skills: Data Mining, Data Processing, Python, Statistical Analysis, Statistics

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About the Employer:
( 74 reviews ) Harrogate, United Kingdom

Project ID: #17927163

34 freelancers are bidding on average $469 for this job


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