Time Series Analysis -------------------- 0. Parse config and command line parameters 1. Read data. Possibly handle a variety of formats Convert to numpy array 2. Select training set 3. Remove outliers Create a rule. Rule+data =>mask. Leave data intact 4. Remove trend Linear interpolation of training data 5. Fit cleaned training data MLP 10 fold CV 6. This gives model data =[outlier rule]=> mask masked data =[de-trend]=> de-trended data (do not recalculate trend) apply model to de-trended data => predicted de-trended data re-trend data => predicted masked data unmask => data with gaps where mask was 6. Test model on test data (=input data - training data) Apply model to test data Compare predictions to actual on unmasked items