# Using EDAs for time series and times series transformation selection

When working with Time series in a Machine Learning project it is very common to try different combinations of the time series in order to perform better the forecasting model. In this section we will apply an EDA to select the optimal subset of variables and time series transformations to improve the model.

```# loading essential libraries first
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.api import VAR
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error

# EDAspy libraries
from EDAspy.timeseries import EDA_ts_fts as EDA
from EDAspy.timeseries import TSTransformations
```
```# import some data
df = mdata.iloc[:, 2:12]
```
```variables = list(df.columns)
variable_y = 'pop'  # pop is the variable we want to forecast
variables = list(set(variables) - {variable_y})  # array of variables to select among transformations
variables

We define a cost function which receives a dictionary with variables names as keys of the dictionary and
values 1/0 if they are used or not respectively.
```
```TSTransf = TSTransformations(df)
transformations = ['detrend', 'smooth', 'log']  # postfix to variables, to denote the transformation

# build the transformations
for var in variables:
transformation = TSTransf.de_trending(var)
df[var + 'detrend'] = transformation

for var in variables:
transformation = TSTransf.smoothing(var, window=10)
df[var + 'smooth'] = transformation

for var in variables:
transformation = TSTransf.log(var)
df[var + 'log'] = transformation
```

Define the cost function to calculate the Mean Absolute Error

```def cost_function(variables_list, nobs=20, maxlags=15, forecastings=10):
"""
variables_list: list of variables without the variable_y
nobs: how many observations for validation
maxlags: previous lags used to predict
forecasting: number of observations to predict

return: MAE of the prediction with the real validation data
"""

data = df[variables_list + [variable_y]]

df_train, df_test = data[0:-nobs], data[-nobs:]

model = VAR(df_train)
results = model.fit(maxlags=maxlags, ic='aic')

lag_order = results.k_ar
array = results.forecast(df_train.values[-lag_order:], forecastings)

variables_ = list(data.columns)
position = variables_.index(variable_y)

validation = [array[i][position] for i in range(len(array))]
mae = mean_absolute_error(validation, df_test['pop'][-forecastings:])

return mae
```

We take the normal variables without any time series transformation and try to forecast the y variable using the same cost function defined. This value is stored to be compared with the optimum solution found

```eda = UMDAd(size_gen=30, max_iter=100, dead_iter=10, n_variables=len(variables), alpha=0.5, vector=None,
lower_bound=0.2, upper_bound=0.9, elite_factor=0.2, disp=True)

eda_result = eda.minimize(cost_function=cost_function, output_runtime=True)
```

Note that the algorithm is minimzing correctly, but doe to the fact that it is a toy example, there is not a high variance from the beginning to the end.

```mae_pre_eda = cost_function(variables)
print('MAE without using EDA:', mae_pre_eda)
```

Initialization of the initial vector of statitstics. Each variable has a 50% probability to be or not chosen

```vector = pd.DataFrame(columns=list(variables))
vector.loc[0] = 0.5
```

Run the algorithm. The code will print some further information during execution

```eda = EDA(max_it=50, dead_it=5, size_gen=15, alpha=0.7, vector=vector,
array_transformations=transformations, cost_function=cost_function)
best_ind, best_MAE = eda.run(output=True)
```

We show some plots of the best solutions found during the execution in each iteration of the algorithm.

```# some plots
hist = eda.historic_best

relative_plot = []
mx = 999999999
for i in range(len(hist)):
if hist[i] < mx:
mx = hist[i]
relative_plot.append(mx)
else:
relative_plot.append(mx)

print('Solution:', best_ind, '\nMAE post EDA: %.2f' % best_MAE, '\nMAE pre EDA: %.2f' % mae_pre_eda)

plt.figure(figsize = (14,6))

ax = plt.subplot(121)
ax.plot(list(range(len(hist))), hist)
ax.title.set_text('Local cost found')
ax.set_xlabel('iteration')
ax.set_ylabel('MAE')

ax = plt.subplot(122)
ax.plot(list(range(len(relative_plot))), relative_plot)
ax.title.set_text('Best global cost found')
ax.set_xlabel('iteration')
ax.set_ylabel('MAE')

plt.show()
```