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Author: Algorithmica

2019-october/18.time series forecasting/timeseries-ar(gridsearch).py

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+import math 
+import pandas as pd
+import numpy as np
+import os
+from sklearn import metrics
+from sklearn.model_selection import TimeSeriesSplit
+from statsmodels.tsa import ar_model
+import matplotlib.pyplot as plt
+
+def grid_search_best_model_timeseries_ar(df, grid, cv):
+    best_param = None
+    best_score = np.infty
+    tsp = TimeSeriesSplit(n_splits=cv)
+            
+    for param in grid.get('lags'):
+        scores = []
+        for train_ind, test_ind in tsp.split(df):
+            train_data = df.iloc[train_ind]
+            test_data = df.iloc[test_ind]
+            try:
+                #print(train_data, test_data)
+                estimator = ar_model.AutoReg(train_data, lags=param)
+                res = estimator.fit() 
+                #print(res.params)
+                #get out of sample predictions with test data start and end
+                pred = estimator.predict(res.params, test_data.index[0], test_data.index[-1])
+                #print(pred)
+                y_pred = pred.values.reshape(-1)
+                y_test = test_data.values.reshape(-1)
+                score = math.sqrt(metrics.mean_squared_error(y_test,  y_pred))
+                scores.append(score)
+            except:
+                pass
+        #print(scores)
+        if len(scores) > 0  and np.mean(scores) < best_score :
+            best_score = np.mean(scores)
+            best_param = param
+        
+    if best_param is not None:
+        estimator = ar_model.AutoReg(df, lags=best_param)
+        res = estimator.fit()
+        print("best parameters:" + str(best_param))
+        print("validation rmse:" +  str(best_score))
+        #get insample predictions with start and end indices
+        predictions = estimator.predict(res.params, start=0, end=df.shape[0]-1 )
+        y_pred = predictions.values.reshape(-1)
+        y_train = df.values.reshape(-1)[best_param:]
+        train_rmse = math.sqrt(metrics.mean_squared_error(y_train,  y_pred))
+        print("train rmse:" + str(train_rmse))
+        return estimator, res
+    else:
+        return None, None
+    
+path = 'F:/'
+df = pd.read_csv(os.path.join(path,'uk-deaths-from-bronchitis-emphys.csv'))
+df.info()
+
+df.columns = ['timestamp', 'y']
+df.index = pd.to_datetime(df['timestamp'], format='%Y-%m')
+df.index.freq = 'MS'
+df.drop('timestamp', axis=1, inplace=True)
+
+#grid search and get final model with best parameters
+ar_grid  = { 'lags':[2,3,4,5] }
+estimator, res = grid_search_best_model_timeseries_ar(df, ar_grid, 3)
+print(res.params)
+print(res.summary())
+plt.plot(df)
+plt.figure()
+res.resid.plot()
+
+#get predictions for future(implicit intervals based on freq of train data)
+start_index = pd.datetime(1980, 1, 1)
+end_index = pd.datetime(1990, 12, 1)
+pred = estimator.predict(res.params, start_index, end_index)
+print(pred)
+
+#get predictions for future(explicit intervals)
+index = pd.date_range('1-1-1980', '12-1-1990', freq='MS')
+pred = estimator.predict(res.params, index[0], index[-1])
+print(pred)
+
+plt.figure()
+plt.plot(pred)

2019-october/18.time series forecasting/timeseries-arima(gridsearch).py

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+import math 
+import pandas as pd
+import numpy as np
+import os
+from sklearn import metrics
+from sklearn.model_selection import TimeSeriesSplit
+from itertools import product
+from statsmodels.tsa import arima_model
+import matplotlib.pyplot as plt
+
+def grid_search_best_model_timeseries_arima(df, grid, cv):
+    keys, values = zip(*grid.items())
+    params =[]
+    for v in product(*values):
+        params.append(tuple(v))
+    
+    print(params)
+    best_param = None
+    best_score = np.infty
+    tsp = TimeSeriesSplit(n_splits=cv)
+            
+    for param in params:
+        scores = []
+        for train_ind, test_ind in tsp.split(df):
+            train_data = df.iloc[train_ind]
+            test_data = df.iloc[test_ind]
+            try:
+                #print(train_data, test_data)
+                estimator = arima_model.ARIMA(train_data, order=param)
+                res = estimator.fit() 
+                #print(res.params)
+                #get out of sample predictions with test data start and end
+                y_pred = estimator.predict(res.params, test_data.index[0], test_data.index[-1])
+                #print(y_pred)
+                y_test = test_data.values.reshape(-1)
+                score = math.sqrt(metrics.mean_squared_error(y_test,  y_pred))
+                scores.append(score)
+            except:
+                pass
+        #print(scores)
+        if len(scores) > 0  and np.mean(scores) < best_score :
+            best_score = np.mean(scores)
+            best_param = param
+        
+    if best_param is not None:
+        estimator = arima_model.ARIMA(df, order=best_param)
+        res = estimator.fit()
+        print("best parameters:" + str(best_param))
+        print("validation rmse:" +  str(best_score))
+        #get insample predictions with start and end indices
+        y_pred = estimator.predict(res.params, start=0, end=df.shape[0]-1 )
+        y_train = df.values.reshape(-1)
+        train_rmse = math.sqrt(metrics.mean_squared_error(y_train,  y_pred))
+        print("train rmse:" + str(train_rmse))
+        return estimator, res
+    else:
+        return None, None
+    
+path = 'F:/'
+df = pd.read_csv(os.path.join(path,'uk-deaths-from-bronchitis-emphys.csv'))
+df.info()
+
+df.columns = ['timestamp', 'y']
+df.index = pd.to_datetime(df['timestamp'], format='%Y-%m')
+df.index.freq = 'MS'
+df.drop('timestamp', axis=1, inplace=True)
+
+#grid search and get final model with best parameters
+arima_grid  = { 'p':[0,1,5,7], 'd':[0,1,2], 'q':[0,1,2,5] }
+estimator, res = grid_search_best_model_timeseries_arima(df, arima_grid, 3)
+print(res.params)
+print(res.summary())
+plt.plot(df)
+plt.figure()
+res.resid.plot()
+
+#get predictions for future(implicit intervals based on freq of train data)
+start_index = pd.datetime(1980, 1, 1)
+end_index = pd.datetime(1990, 12, 1)
+pred = estimator.predict(res.params, start_index, end_index)
+print(pred)
+
+#get predictions for future(explicit intervals)
+index = pd.date_range('1-1-1980', '12-1-1990', freq='MS')
+pred = estimator.predict(res.params, index[0], index[-1])
+pred = pd.Series(pred, index=index)
+print(pred)
+
+plt.figure()
+plt.plot(pred)

2019-october/18.time series forecasting/timeseries-arma(gridsearch).py

@@ -0,0 +1,90 @@
+import math 
+import pandas as pd
+import numpy as np
+import os
+from sklearn import metrics
+from sklearn.model_selection import TimeSeriesSplit
+from itertools import product
+from statsmodels.tsa import arima_model
+import matplotlib.pyplot as plt
+
+def grid_search_best_model_timeseries_arma(df, grid, cv):
+    keys, values = zip(*grid.items())
+    params =[]
+    for v in product(*values):
+        params.append(tuple(v))
+    
+    print(params)
+    best_param = None
+    best_score = np.infty
+    tsp = TimeSeriesSplit(n_splits=cv)
+            
+    for param in params:
+        scores = []
+        for train_ind, test_ind in tsp.split(df):
+            train_data = df.iloc[train_ind]
+            test_data = df.iloc[test_ind]
+            try:
+                #print(train_data, test_data)
+                estimator = arima_model.ARMA(train_data, order=param)
+                res = estimator.fit() 
+                #print(res.params)
+                #get out of sample predictions with test data start and end
+                y_pred = estimator.predict(res.params, test_data.index[0], test_data.index[-1])
+                #print(y_pred)
+                y_test = test_data.values.reshape(-1)
+                score = math.sqrt(metrics.mean_squared_error(y_test,  y_pred))
+                scores.append(score)
+            except:
+                pass
+        #print(scores)
+        if len(scores) > 0  and np.mean(scores) < best_score :
+            best_score = np.mean(scores)
+            best_param = param
+        
+    if best_param is not None:
+        estimator = arima_model.ARMA(df, order=best_param)
+        res = estimator.fit()
+        print("best parameters:" + str(best_param))
+        print("validation rmse:" +  str(best_score))
+        #get insample predictions with start and end indices
+        y_pred = estimator.predict(res.params, start=0, end=df.shape[0]-1 )
+        y_train = df.values.reshape(-1)
+        train_rmse = math.sqrt(metrics.mean_squared_error(y_train,  y_pred))
+        print("train rmse:" + str(train_rmse))
+        return estimator, res
+    else:
+        return None, None
+    
+path = 'F:/'
+df = pd.read_csv(os.path.join(path,'uk-deaths-from-bronchitis-emphys.csv'))
+df.info()
+
+df.columns = ['timestamp', 'y']
+df.index = pd.to_datetime(df['timestamp'], format='%Y-%m')
+df.index.freq = 'MS'
+df.drop('timestamp', axis=1, inplace=True)
+
+#grid search and get final model with best parameters
+arma_grid  = { 'p':[0,1,2,3,5,7], 'q':[1,2,3,5,7] }
+estimator, res = grid_search_best_model_timeseries_arma(df, arma_grid, 3)
+print(res.params)
+print(res.summary())
+plt.plot(df)
+plt.figure()
+res.resid.plot()
+
+#get predictions for future(implicit intervals based on freq of train data)
+start_index = pd.datetime(1980, 1, 1)
+end_index = pd.datetime(1990, 12, 1)
+pred = estimator.predict(res.params, start_index, end_index)
+print(pred)
+
+#get predictions for future(explicit intervals)
+index = pd.date_range('1-1-1980', '12-1-1990', freq='MS')
+pred = estimator.predict(res.params, index[0], index[-1])
+pred = pd.Series(pred, index=index)
+print(pred)
+
+plt.figure()
+plt.plot(pred)

2019-october/18.time series forecasting/timeseries-cross validation.py

@@ -0,0 +1,23 @@
+import pandas as pd
+import os
+from sklearn.model_selection import TimeSeriesSplit
+import statsmodels
+print(statsmodels.__version__)
+
+path = 'F:/'
+df = pd.read_csv(os.path.join(path,'uk-deaths-from-bronchitis-emphys.csv'))
+df.info()
+
+df.columns = ['timestamp', 'y']
+df.index = pd.to_datetime(df['timestamp'], format='%Y-%m')
+df.drop('timestamp', axis=1, inplace=True)
+
+tsp = TimeSeriesSplit(n_splits=3)
+
+for train_ind, test_ind in tsp.split(df):
+     print(train_ind, test_ind)
+     
+for train_ind, test_ind in tsp.split(df):
+     train_data = df.iloc[train_ind]
+     test_data = df.iloc[test_ind]
+     print(train_data, test_data)

2019-october/18.time series forecasting/timeseries-model building and evaluation.py

@@ -0,0 +1,48 @@
+import math 
+import pandas as pd
+import numpy as np
+import os
+from sklearn import metrics
+from sklearn.model_selection import TimeSeriesSplit
+from statsmodels.tsa import ar_model
+
+path = 'F:/'
+df = pd.read_csv(os.path.join(path,'uk-deaths-from-bronchitis-emphys.csv'))
+df.info()
+
+df.columns = ['timestamp', 'y']
+df.index = pd.to_datetime(df['timestamp'], format='%Y-%m').copy()
+df.index.freq = 'MS'
+df.drop('timestamp', axis=1, inplace=True)
+
+#build model
+estimator = ar_model.AutoReg(df, lags=5)
+res = estimator.fit()
+print(res.params)
+print(res.model)
+print(res.summary())
+
+#using model
+predictions = estimator.predict(res.params, start=0, end=df.shape[0]-1 )
+print(predictions)
+y_pred = predictions.values.reshape(-1)
+y_train = df.values.reshape(-1)[5:]
+train_rmse = math.sqrt(metrics.mean_squared_error(y_train,  y_pred))
+print(train_rmse)
+ 
+#evaluate model
+tsp = TimeSeriesSplit(n_splits=3)
+scores = []
+
+for train_ind, test_ind in tsp.split(df):
+     train_data = df.iloc[train_ind]
+     test_data = df.iloc[test_ind]
+     estimator =  ar_model.AutoReg(df, lags=5)
+     res = estimator.fit()
+     pred = estimator.predict(res.params,test_data.index[0], test_data.index[-1])
+     y_pred = pred.values.reshape(-1)
+     y_test = test_data.values.reshape(-1)
+     score = math.sqrt(metrics.mean_squared_error(y_test,  y_pred))
+     scores.append(score)
+print(scores)
+print(np.mean(scores))