Introduction to Time Series Forecasting With Python: How to Prepare Data and Develop Models to Predict the FutureTime series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data. |
Contents
II Data Preparation | 20 |
III Temporal Structure | 85 |
IV Evaluate Models | 144 |
V Forecast Models | 182 |
VI Projects | 247 |
VII Conclusions | 335 |
VIII Appendix | 339 |
Common terms and phrases
analysis ARIMA model autocorrelation autoregression bias calculate called complete components correlation create Daily Female Births Daily Temperatures dataset density plots develop differenced discover distribution evaluate Example output expected Female Births dataset Figure follows forecast error function header=0 histogram index_col=0 input interval learning lesson line plot linear Listing load look machine learning matplotlib import pyplot mean method Minimum Daily Temperatures model_fit monthly months moving average observations pandas import read_csv parameters parse_dates=True performance persistence model predictions prepare prints problem provides pyplot.show Python random walk read_csv from pandas remove residual errors result rmse root rows Running the example seasonality series data series dataset series forecasting Shampoo Shampoo Sales dataset shows Specifically split squared squeeze=True stationary step structure suggests summary statistics supervised learning train transform trend tutorial validation values variables window yhat