Open pintuiitbhi opened 5 years ago
I trained a HOFTS with order 3 on erratic data. Dataset is montly. I predicted on train data and it looks like it has overfitted.
Here is the code:
from pyFTS.models import hofts, pwfts fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,8]) ax.plot(train_uv[:100], label='Original') rows = [] # for method in [hofts.HighOrderFTS, hofts.WeightedHighOrderFTS, pwfts.ProbabilisticWeightedFTS]: for method in [hofts.HighOrderFTS]: # for order in [1, 2,3]: for order in [3]: model = method(partitioner=part, order=order) model.shortname += str(order) model.fit(train_uv) forecasts = model.predict(train_uv) # forecasts = model.predict([0,792,492], steps_ahead=142) forecast_fuzzy = forecasts for k in np.arange(order): forecasts.insert(0,None) ax.plot(forecasts[:100], label=model.shortname) models.append(model.shortname) # Util.persist_obj(model, model.shortname) # del(model) handles, labels = ax.get_legend_handles_labels() lgd = ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1))
I want to predict next 3 months data ie. 12 data points. How I can use the "predict" function to do this?
This is how I tried.
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,8]) ax.plot(test_uv, label='Original') forecasts = model.predict([1068,2280,4392], steps_ahead=12) order = 3 for k in np.arange(order): forecasts.insert(0,None) ax.plot(forecasts, label=model.shortname) handles, labels = ax.get_legend_handles_labels() lgd = ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1))
Why is it remains constant after predicting 3 points in future?
[1068,2280,4392] is the last three data points of train dataset.
Originally posted by @pintuiitbhi in https://github.com/PYFTS/pyFTS/issues/6#issuecomment-499391674
I trained a HOFTS with order 3 on erratic data. Dataset is montly. I predicted on train data and it looks like it has overfitted.
Here is the code:
I want to predict next 3 months data ie. 12 data points. How I can use the "predict" function to do this?
This is how I tried.
Why is it remains constant after predicting 3 points in future?
Originally posted by @pintuiitbhi in https://github.com/PYFTS/pyFTS/issues/6#issuecomment-499391674