Time Series Forecasting for Humidity
1
I have following input values and wants to predict the humidity values for the values present in timestamps list
startDate = "2013-01-01"
endDate = "2013-01-01"
knownTimestamps = ['2013-01-01 00:00','2013-01-01 01:00','2013-01-01 02:00','2013-01-01 03:00','2013-01-01 04:00',
'2013-01-01 05:00','2013-01-01 06:00','2013-01-01 08:00','2013-01-01 10:00','2013-01-01 11:00',
'2013-01-01 12:00','2013-01-01 13:00','2013-01-01 16:00','2013-01-01 17:00','2013-01-01 18:00',
'2013-01-01 19:00','2013-01-01 20:00','2013-01-01 21:00','2013-01-01 23:00']
humidity = ['0.62','0.64','0.62','0.63','0.63','0.64','0.63','0.64','0.48','0.46','0.45','0.44','0.46','0.47','0.48','0.49','0.51','0.52','0.52']
timestamps = ['2013-01-01 07:00','2013-01-01 09:00','2013-01-01 14:00','2013-01-01 15:00','2013-01-01 22:00']
and I am using following function to predict the humidity values using AR model in python
from statsmodels.tsa.arima_model import ARIMA
def predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps):
data_prediction = pd.DataFrame({'knownTimestamps': knownTimestamps,'humidity': humidity})
print(data_prediction.head(10))
history = [float(x) for x in data_prediction.humidity]
predictions =
test = timestamps
for t in range(len(test)):
model = ARIMA(history, order=(2,2,0))
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
predictions.append(float(yhat))
print(predictions)
return predictions
The model predict the same value of humidity for the values in time stamp list.
res = predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps)
print(res)
output = [0.5287247355700563, 0.5287247355700563, 0.5287247355700563,
0.5287247355700563, 0.5287247355700563]
Can someone help me with where I am going wrong
python pandas machine-learning time-series arima
asked Jan 7 at 2:32
niranjan272
108
add a comment |
1
I have following input values and wants to predict the humidity values for the values present in timestamps list
startDate = "2013-01-01"
endDate = "2013-01-01"
knownTimestamps = ['2013-01-01 00:00','2013-01-01 01:00','2013-01-01 02:00','2013-01-01 03:00','2013-01-01 04:00',
'2013-01-01 05:00','2013-01-01 06:00','2013-01-01 08:00','2013-01-01 10:00','2013-01-01 11:00',
'2013-01-01 12:00','2013-01-01 13:00','2013-01-01 16:00','2013-01-01 17:00','2013-01-01 18:00',
'2013-01-01 19:00','2013-01-01 20:00','2013-01-01 21:00','2013-01-01 23:00']
humidity = ['0.62','0.64','0.62','0.63','0.63','0.64','0.63','0.64','0.48','0.46','0.45','0.44','0.46','0.47','0.48','0.49','0.51','0.52','0.52']
timestamps = ['2013-01-01 07:00','2013-01-01 09:00','2013-01-01 14:00','2013-01-01 15:00','2013-01-01 22:00']
and I am using following function to predict the humidity values using AR model in python
from statsmodels.tsa.arima_model import ARIMA
def predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps):
data_prediction = pd.DataFrame({'knownTimestamps': knownTimestamps,'humidity': humidity})
print(data_prediction.head(10))
history = [float(x) for x in data_prediction.humidity]
predictions =
test = timestamps
for t in range(len(test)):
model = ARIMA(history, order=(2,2,0))
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
predictions.append(float(yhat))
print(predictions)
return predictions
The model predict the same value of humidity for the values in time stamp list.
res = predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps)
print(res)
output = [0.5287247355700563, 0.5287247355700563, 0.5287247355700563,
0.5287247355700563, 0.5287247355700563]
Can someone help me with where I am going wrong
python pandas machine-learning time-series arima
asked Jan 7 at 2:32
niranjan272
108
I don't see you returningpredictionsin the function or calling the function.
– Optimesh
Jan 7 at 13:19
yes I didn't include that part in the question, I'll make the changes
– niranjan272
Jan 10 at 1:56
add a comment |
1
1
1
1
I have following input values and wants to predict the humidity values for the values present in timestamps list
startDate = "2013-01-01"
endDate = "2013-01-01"
knownTimestamps = ['2013-01-01 00:00','2013-01-01 01:00','2013-01-01 02:00','2013-01-01 03:00','2013-01-01 04:00',
'2013-01-01 05:00','2013-01-01 06:00','2013-01-01 08:00','2013-01-01 10:00','2013-01-01 11:00',
'2013-01-01 12:00','2013-01-01 13:00','2013-01-01 16:00','2013-01-01 17:00','2013-01-01 18:00',
'2013-01-01 19:00','2013-01-01 20:00','2013-01-01 21:00','2013-01-01 23:00']
humidity = ['0.62','0.64','0.62','0.63','0.63','0.64','0.63','0.64','0.48','0.46','0.45','0.44','0.46','0.47','0.48','0.49','0.51','0.52','0.52']
timestamps = ['2013-01-01 07:00','2013-01-01 09:00','2013-01-01 14:00','2013-01-01 15:00','2013-01-01 22:00']
and I am using following function to predict the humidity values using AR model in python
from statsmodels.tsa.arima_model import ARIMA
def predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps):
data_prediction = pd.DataFrame({'knownTimestamps': knownTimestamps,'humidity': humidity})
print(data_prediction.head(10))
history = [float(x) for x in data_prediction.humidity]
predictions =
test = timestamps
for t in range(len(test)):
model = ARIMA(history, order=(2,2,0))
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
predictions.append(float(yhat))
print(predictions)
return predictions
The model predict the same value of humidity for the values in time stamp list.
res = predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps)
print(res)
output = [0.5287247355700563, 0.5287247355700563, 0.5287247355700563,
0.5287247355700563, 0.5287247355700563]
Can someone help me with where I am going wrong
python pandas machine-learning time-series arima
asked Jan 7 at 2:32
niranjan272
108
I have following input values and wants to predict the humidity values for the values present in timestamps list
startDate = "2013-01-01"
endDate = "2013-01-01"
knownTimestamps = ['2013-01-01 00:00','2013-01-01 01:00','2013-01-01 02:00','2013-01-01 03:00','2013-01-01 04:00',
'2013-01-01 05:00','2013-01-01 06:00','2013-01-01 08:00','2013-01-01 10:00','2013-01-01 11:00',
'2013-01-01 12:00','2013-01-01 13:00','2013-01-01 16:00','2013-01-01 17:00','2013-01-01 18:00',
'2013-01-01 19:00','2013-01-01 20:00','2013-01-01 21:00','2013-01-01 23:00']
humidity = ['0.62','0.64','0.62','0.63','0.63','0.64','0.63','0.64','0.48','0.46','0.45','0.44','0.46','0.47','0.48','0.49','0.51','0.52','0.52']
timestamps = ['2013-01-01 07:00','2013-01-01 09:00','2013-01-01 14:00','2013-01-01 15:00','2013-01-01 22:00']
and I am using following function to predict the humidity values using AR model in python
from statsmodels.tsa.arima_model import ARIMA
def predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps):
data_prediction = pd.DataFrame({'knownTimestamps': knownTimestamps,'humidity': humidity})
print(data_prediction.head(10))
history = [float(x) for x in data_prediction.humidity]
predictions =
test = timestamps
for t in range(len(test)):
model = ARIMA(history, order=(2,2,0))
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
predictions.append(float(yhat))
print(predictions)
return predictions
The model predict the same value of humidity for the values in time stamp list.
res = predictMissingHumidity(startDate, endDate, knownTimestamps, humidity, timestamps)
print(res)
output = [0.5287247355700563, 0.5287247355700563, 0.5287247355700563,
0.5287247355700563, 0.5287247355700563]
Can someone help me with where I am going wrong
python pandas machine-learning time-series arima
python pandas machine-learning time-series arima
asked Jan 7 at 2:32
niranjan272
108
asked Jan 7 at 2:32
niranjan272
108
edited Dec 2 at 3:04
asked Jan 7 at 2:32
niranjan272
108
asked Jan 7 at 2:32
niranjan272
108
asked Jan 7 at 2:32
niranjan272
108
108
I don't see you returningpredictionsin the function or calling the function.
– Optimesh
Jan 7 at 13:19
yes I didn't include that part in the question, I'll make the changes
– niranjan272
Jan 10 at 1:56
add a comment |
I don't see you returningpredictionsin the function or calling the function.
– Optimesh
Jan 7 at 13:19
yes I didn't include that part in the question, I'll make the changes
– niranjan272
Jan 10 at 1:56
I don't see you returning
predictions in the function or calling the function.– Optimesh
Jan 7 at 13:19
I don't see you returning
predictions in the function or calling the function.– Optimesh
Jan 7 at 13:19
yes I didn't include that part in the question, I'll make the changes
– niranjan272
Jan 10 at 1:56
yes I didn't include that part in the question, I'll make the changes
– niranjan272
Jan 10 at 1:56
add a comment |
1 Answer
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For me it looks you just repeat same calculation n times where n is len(test). The iteration variable t is never used and all arguments are the same every time.
answered Jun 13 at 13:34
Sasha Shipka
2114
add a comment |
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1 Answer
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1 Answer
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active
oldest
votes
active
oldest
votes
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oldest
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2
For me it looks you just repeat same calculation n times where n is len(test). The iteration variable t is never used and all arguments are the same every time.
answered Jun 13 at 13:34
Sasha Shipka
2114
add a comment |
2
For me it looks you just repeat same calculation n times where n is len(test). The iteration variable t is never used and all arguments are the same every time.
answered Jun 13 at 13:34
Sasha Shipka
2114
add a comment |
2
2
2
For me it looks you just repeat same calculation n times where n is len(test). The iteration variable t is never used and all arguments are the same every time.
answered Jun 13 at 13:34
Sasha Shipka
2114
For me it looks you just repeat same calculation n times where n is len(test). The iteration variable t is never used and all arguments are the same every time.
answered Jun 13 at 13:34
Sasha Shipka
2114
answered Jun 13 at 13:34
Sasha Shipka
2114
answered Jun 13 at 13:34
Sasha Shipka
2114
answered Jun 13 at 13:34
Sasha Shipka
2114
2114
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add a comment |
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I don't see you returning
predictionsin the function or calling the function.– Optimesh
Jan 7 at 13:19
yes I didn't include that part in the question, I'll make the changes
– niranjan272
Jan 10 at 1:56