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Forecasting-Bus-Demand Project.py
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import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from sklearn.metrics import mean_squared_error
from math import sqrt
import warnings
df = pd.read_csv('municipality_bus_utilization.csv')
# convert the timestamp column to datetime
df['timestamp'] = pd.to_datetime(df['timestamp'])
# extract hour from the timestamp column to create an time_hour column
hour = df['hour'] = df['timestamp'].dt.hour
#minute = df['minute'] = df['timestamp'].dt.minute
time = df['time'] = df['timestamp'].dt.date
print(df['timestamp'])
municipaly_id = df.iloc[:,1:2].values
municipaly_id = pd.DataFrame(data=municipaly_id, columns = ['municipaly_id'])
print(municipaly_id)
#Encoder
#from sklearn import preprocessing
#le = preprocessing.LabelEncoder()
#municipaly_id[:,0] = le.fit_transform(df.iloc[:,1:2])
#print(municipaly_id)
#ohe = preprocessing.OneHotEncoder()
#municipaly_id = ohe.fit_transform(municipaly_id).toarray()
#print(municipaly_id)
#mid = pd.DataFrame(data=municipaly_id, columns = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
#print(mid)
others = df.iloc[:,2:4].values
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
impdata = others[:,:]
imputer = imputer.fit(impdata[:,:])
impdata[:,:] = imputer.transform(impdata[:,:])
finalothers = impdata[:,:]
finalothers = pd.DataFrame(data=finalothers, columns = ['usage', 'total_capacity'])
print(finalothers)
data = pd.concat([df['timestamp'], municipaly_id], axis=1)
print(data)
data = pd.concat([data, finalothers], axis=1)
print(data)
traindata = data.iloc[:10390,:]
testdata = data.iloc[10390:,:]
traindata = traindata.sort_values(["municipaly_id","timestamp"], ascending = (True,True))
testdata = testdata.sort_values(["municipaly_id","timestamp"], ascending = (True,True))
print(traindata)
traindata0 = traindata.iloc[:1039,:]
traindata1 = traindata.iloc[1039:2078,:]
traindata2 = traindata.iloc[2078:3117,:]
traindata3 = traindata.iloc[3117:4156,:]
traindata4 = traindata.iloc[4156:5195,:]
traindata5 = traindata.iloc[5195:6234,:]
traindata6 = traindata.iloc[6234:7273,:]
traindata7 = traindata.iloc[7273:8312,:]
traindata8 = traindata.iloc[8312:9351,:]
traindata9 = traindata.iloc[9351:,:]
print(traindata0)
print(traindata1)
print(traindata2)
print(traindata3)
print(traindata4)
print(traindata5)
print(traindata6)
print(traindata7)
print(traindata8)
print(traindata9)
#a = traindata.filter(columns = [regex='0$')
#print(a)
print(testdata)
testdata0 = testdata.iloc[:268,:]
testdata1 = testdata.iloc[268:536,:]
testdata2 = testdata.iloc[536:804,:]
testdata3 = testdata.iloc[804:1072,:]
testdata4 = testdata.iloc[1072:1340,:]
testdata5 = testdata.iloc[1340:1608,:]
testdata6 = testdata.iloc[1608:1876,:]
testdata7 = testdata.iloc[1876:2144,:]
testdata8 = testdata.iloc[2144:2412,:]
testdata9 = testdata.iloc[2412:,:]
print(testdata0)
print(testdata1)
print(testdata2)
print(testdata3)
print(testdata4)
print(testdata5)
print(testdata6)
print(testdata7)
print(testdata8)
print(testdata9)
finaldata = traindata.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
finaldata0 = traindata0.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata0 = traindata0.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata0)
finaldata1 = traindata1.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata1 = traindata1.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata1)
finaldata2 = traindata2.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata2 = traindata2.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata2)
finaldata3 = traindata3.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata3 = traindata3.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata3)
finaldata4 = traindata4.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata4 = traindata4.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata4)
finaldata5 = traindata5.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata5 = traindata5.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata5)
finaldata6 = traindata6.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata6 = traindata6.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata6)
finaldata7 = traindata7.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata7 = traindata7.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata7)
finaldata8 = traindata8.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata8 = traindata8.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata8)
finaldata9 = traindata9.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tsdata9 = traindata9.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tsdata9)
print(finaldata)
print(finaldata0)
print(finaldata1)
print(finaldata2)
print(finaldata3)
print(finaldata4)
print(finaldata5)
print(finaldata6)
print(finaldata7)
print(finaldata8)
print(finaldata9)
trydata = testdata.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
trydata0 = testdata0.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata0 = testdata0.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(trydata0)
trydata1 = testdata1.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata1 = testdata1.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata1)
trydata2 = testdata2.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata2 = testdata2.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata2)
trydata3 = testdata3.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata3 = testdata3.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata3)
trydata4 = testdata4.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata4 = testdata4.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata4)
trydata5 = testdata5.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata5 = testdata5.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata5)
trydata6 = testdata6.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata6 = testdata6.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata6)
trydata7 = testdata7.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata7 = testdata7.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata7)
trydata8 = testdata8.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata8 = testdata8.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata8)
trydata9 = testdata9.groupby([pd.Grouper(key='timestamp', freq='H'), 'municipaly_id'], as_index=False)['usage'].max()
tstdata9 = testdata9.groupby([pd.Grouper(key='timestamp', freq='H')])['usage'].max()
print(tstdata9)
print(trydata)
print(trydata0)
print(trydata1)
print(trydata2)
print(trydata3)
print(trydata4)
print(trydata5)
print(trydata6)
print(trydata7)
print(trydata8)
print(trydata9)
warnings.filterwarnings("ignore")
# Predict0
model0 = ExponentialSmoothing(finaldata0["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model0 = model0.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred0 = hw_model0.predict(start=trydata0["usage"].index[0], end=trydata0["usage"].index[-1])
pred0.index += len(finaldata0)
trydata0.index += len(finaldata0)
print(pred0)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata0["usage"].index, finaldata0["usage"], label='Train'); plt.plot(pred0.index, pred0, 'b-', label = 'Predicted Data'); plt.plot(trydata0["usage"].index, trydata0["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 0'); plt.legend(loc="best");
# Predict1
model1 = ExponentialSmoothing(finaldata1["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model1 = model1.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred1 = hw_model1.predict(start=trydata1["usage"].index[0], end=trydata1["usage"].index[-1])
pred1.index += len(finaldata1)
trydata1.index += len(finaldata1)
print(pred1)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata1["usage"].index, finaldata1["usage"], label='Train'); plt.plot(pred1.index, pred1, 'b-', label = 'Predicted Data'); plt.plot(trydata1["usage"].index, trydata1["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 1'); plt.legend(loc="best");
# Predict2
model2 = ExponentialSmoothing(finaldata2["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model2 = model2.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred2 = hw_model2.predict(start=trydata2["usage"].index[0], end=trydata2["usage"].index[-1])
pred2.index += len(finaldata2)
trydata2.index += len(finaldata2)
print(pred2)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata2["usage"].index, finaldata2["usage"], label='Train'); plt.plot(pred2.index, pred2, 'b-', label = 'Predicted Data'); plt.plot(trydata2["usage"].index, trydata2["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 2'); plt.legend(loc="best");
# Predict3
model3 = ExponentialSmoothing(finaldata3["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model3 = model3.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred3 = hw_model3.predict(start=trydata3["usage"].index[0], end=trydata3["usage"].index[-1])
pred3.index += len(finaldata3)
trydata3.index += len(finaldata3)
print(pred3)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata3["usage"].index, finaldata3["usage"], label='Train'); plt.plot(pred3.index, pred3, 'b-', label = 'Predicted Data'); plt.plot(trydata3["usage"].index, trydata3["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 3'); plt.legend(loc="best");
# Predict4
model4 = ExponentialSmoothing(finaldata4["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model4 = model4.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred4 = hw_model4.predict(start=trydata4["usage"].index[0], end=trydata4["usage"].index[-1])
pred4.index += len(finaldata4)
trydata4.index += len(finaldata4)
print(pred4)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata4["usage"].index, finaldata4["usage"], label='Train'); plt.plot(pred4.index, pred4, 'b-', label = 'Predicted Data'); plt.plot(trydata4["usage"].index, trydata4["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 4'); plt.legend(loc="best");
# Predict5
model5 = ExponentialSmoothing(finaldata5["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model5 = model5.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred5 = hw_model5.predict(start=trydata5["usage"].index[0], end=trydata5["usage"].index[-1])
pred5.index += len(finaldata5)
trydata5.index += len(finaldata5)
print(pred5)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata5["usage"].index, finaldata5["usage"], label='Train'); plt.plot(pred5.index, pred5, 'b-', label = 'Predicted Data'); plt.plot(trydata5["usage"].index, trydata5["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 5'); plt.legend(loc="best");
# Predict6
model6 = ExponentialSmoothing(finaldata6["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model6 = model6.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred6 = hw_model6.predict(start=trydata6["usage"].index[0], end=trydata6["usage"].index[-1])
pred6.index += len(finaldata6)
trydata6.index += len(finaldata6)
print(pred6)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata6["usage"].index, finaldata6["usage"], label='Train'); plt.plot(pred6.index, pred6, 'b-', label = 'Predicted Data'); plt.plot(trydata6["usage"].index, trydata6["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 6'); plt.legend(loc="best");
# Predict7
model7 = ExponentialSmoothing(finaldata7["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model7 = model7.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred7 = hw_model7.predict(start=trydata7["usage"].index[0], end=trydata7["usage"].index[-1])
pred7.index += len(finaldata7)
trydata7.index += len(finaldata7)
print(pred7)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata7["usage"].index, finaldata7["usage"], label='Train'); plt.plot(pred7.index, pred7, 'b-', label = 'Predicted Data'); plt.plot(trydata7["usage"].index, trydata7["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 7'); plt.legend(loc="best");
# Predict8
model8 = ExponentialSmoothing(finaldata8["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model8 = model8.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred8 = hw_model8.predict(start=trydata8["usage"].index[0], end=trydata8["usage"].index[-1])
pred8.index += len(finaldata8)
trydata8.index += len(finaldata8)
print(pred8)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata8["usage"].index, finaldata8["usage"], label='Train'); plt.plot(pred8.index, pred8, 'b-', label = 'Predicted Data'); plt.plot(trydata8["usage"].index, trydata8["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 8'); plt.legend(loc="best");
# Predict9
model9 = ExponentialSmoothing(finaldata9["usage"], trend=None, seasonal=None, seasonal_periods=None)
hw_model9 = model9.fit(optimized=True, use_boxcox=False, remove_bias=False)
pred9 = hw_model9.predict(start=trydata9["usage"].index[0], end=trydata9["usage"].index[-1])
pred9.index += len(finaldata9)
trydata9.index += len(finaldata9)
print(pred9)
plt.figure(figsize=(20, 6), dpi=200);plt.plot(finaldata9["usage"].index, finaldata9["usage"], label='Train'); plt.plot(pred9.index, pred9, 'b-', label = 'Predicted Data'); plt.plot(trydata9["usage"].index, trydata9["usage"], 'r-', label = 'Real Data'); plt.xlabel('Hours'); plt.ylabel('Bus'); plt.title('Forecasting 9'); plt.legend(loc="best");