Cheatography
https://cheatography.com
Basic commands for time series in Python.
This is a draft cheat sheet. It is a work in progress and is not finished yet.
Imports
from statsmodels.tsa.holtwinters import ExponentialSmoothing
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Steps to fit the model and check it
train = df.iloc[:n] |
use iloc to split the original dataset |
fitted_mod = ExponentialSmoothing(train,
trend='mul or add',
seasonal='mul or add',
seasonal_periods=n_unit).fit() |
create and fit the model |
predictions = fitted_mod.forecast(n of units) |
forecast |
train.plot()
test.plot()
predictions.plot() |
plot forecasted values together with train and test data |
Evaluation metrics
from sklearn.metrics import mean_squared_error, mean_absolute_error |
import the necessary libraries |
mean_squared_error(test, predictions) |
calculate the MSE |
np.sqrt(mean_squared_error(test, predictions)) |
calculate the RMSE |
now that we saw our model was not that far off (if that's the case), we retrain our model on the entire dataset and we can plot it to show the future behaviour of our data
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IMPORTANT CONCEPTS
STATIONARY data: these kinds of data do not exhibit trends or seasonality.
NON-STATIONARY data: these kinds of data exhibit trends or seasonality. |
- via code...
from statmodels.tsa.statespace.tools import diff |
import libraries |
diff(df["timeseries col"], k_diff=1) |
use the diff() func to check stationarity |
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