A project for time series modelling work for demand forecasting in the transport services industry.
Forecasting: Principles and Practice:
https://otexts.com/fpp3/
Breusch_Godfrey Test for no Residual Autocorrelation:
https://www.statsmodels.org/stable/generated/statsmodels.stats.diagnostic.acorr_breusch_godfrey.html
https://www.statology.org/breusch-godfrey-test-python/
Dispatch Dictionary:
https://towardsdatascience.com/dictionary-as-an-alternative-to-if-else-76fe57a1e4af
https://realpython.com/python-lambda/
CV and Hyperparameter Tuning:
https://machinelearningmastery.com/how-to-grid-search-sarima-model-hyperparameters-for-time-series-forecasting-in-python/
https://towardsdatascience.com/time-series-analysis-modeling-validation-386378cd3369
https://towardsdatascience.com/how-good-are-your-forecasts-a37cb9cf5c1d
Model Evaluation:
https://towardsdatascience.com/how-good-are-your-forecasts-a37cb9cf5c1d
Multivariate Time Series Modeling using Facebook Prophet: https://www.youtube.com/watch?v=XZhPO043lqU
https://towardsdatascience.com/forecast-model-tuning-with-additional-regressors-in-prophet-ffcbf1777dda
PCA:
https://www.youtube.com/watch?v=QdBy02ExhGI
https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60
Prophet Model:
https://facebook.github.io/prophet/docs/quick_start.html
https://medium.com/analytics-vidhya/time-series-analysis-using-prophet-in-python-part-1-math-explained-5936509c175c
https://medium.com/analytics-vidhya/time-series-analysis-using-prophet-in-python-part-2-hyperparameter-tuning-and-cross-validation-88e7d831a067
Resampling:
https://machinelearningmastery.com/resample-interpolate-time-series-data-python/
Shapiro-Wilk Test:
https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/)
White Test for Constant Residual Variance:
https://www.statsmodels.org/stable/generated/statsmodels.stats.diagnostic.het_white.html
https://medium.com/@remycanario17/tests-for-heteroskedasticity-in-python-208a0fdb04ab)
https://www.youtube.com/watch?v=jy2OurbLFGg