def plotErrorCorrection(prices, forecast, preds, company):
datesF = list(forecast.index.values)[1:]
datesP = list(prices.index.values)
valuesF = list(forecast['FORECAST'].values)[1:]
valuesP = list(prices['PRC'].values)
colorF = list(forecast['OFFSET'].values)[1:]
datesPred = list(preds['MONTH'].values)
valuesPred = list(preds['VALUE'].values)
variationP = list([0]*len(valuesPred))
for i in range(len(datesPred)):
datesPred[i] = datesPred[i]+12
minC = min(variationP)
maxC = max(variationP)
'''variationP = list(forecast['VALUES'].values)
variationP = np.nan_to_num(variationP)
variation = [-val for val in variationP]
print(variation)
minC = min(variation)
maxC = max(variation)
print(variation)
print(forecast)'''
ploting_utilities.plots2D([datesP,datesF,datesPred],
[valuesP,valuesF,valuesPred],
[None,None,variationP],
[False,False,True],
company,
minC,
maxC)
def test():
x1 = [10, 11, 12, 13]
y1 = [100, 101, 102, 103]
x2 = [11, 12, 13, 14]
y2 = [101, 102, 103, 104]
ploting_utilities.plots2D([x1, x2], [y1, y2])
def plotRelative(prices, preds, grades, forecast, company):
datesF = list(forecast.index.values)
for i in range(len(datesF)):
datesF[i] = datesF[i]
forecasts = list(forecast['FORECAST'].values)
valprice = list(prices['PRC'].values)
datesprice = list(prices.index.values)
gradeDate = [i+12 for i in grades['MONTH'].values]
gradePreds = list(grades['VALUE'].values)
gradeGrades = list(grades['GRADE'].values)
ploting_utilities.plots2D([datesF, datesprice, gradeDate],
[forecasts, valprice, gradePreds],
[None,None,gradeGrades],
[False,False,True],
company)
def plotAbsolute(prices, preds, grades, forecast, company):
datesF = list(forecast['MONTH'].values)
for i in range(len(datesF)):
datesF[i] = datesF[i]
forecasts = list(forecast['FORECAST'].values)
bestF = list(forecast['BEST_FORECAST'].values)
straightF = list(forecast['STRAIGHT_FORECAST'].values)
valprice = list(prices['PRC'].values)
datesprice = list(prices.index.values)
gradeDate = list(grades['MONTH'].values)
gradePreds = list(grades['FORECAST'].values)
gradeGrades = list(grades['GRADE'].values)
ploting_utilities.plots2D(
[datesF, datesprice, datesF, datesF, gradeDate],
[forecasts, valprice, bestF, straightF, gradePreds],
[None, None, None, None, gradeGrades],
[False, False, False, False, True], company)
def plotShortTerm(prices, forecast, company):
datesF = list(forecast.index.values)[1:]
datesP = list(prices.index.values)
valuesF = list(forecast['FORECAST'].values)[1:]
valuesP = list(prices['PRC'].values)
variationP = list(forecast['FORECAST'].values)
variationP = np.nan_to_num(variationP)
variation = [-val for val in variationP]
print(variation)
minC = min(variation)
maxC = max(variation)
print(variation)
print(forecast)
ploting_utilities.plots2D([datesP, datesF], [valuesP, valuesF],
[None, variation], [False, True], company, minC,
maxC)