Time-Series-Analysis

This notebook provides some skills to perform Time-Series-Analysis.

Skills that are covered in this notebook:

1. The datetime module
2. Time Series with Pandas
3. Time Resampling
4. Time Shifting
5. Rolling and Expanding
6. Visualizing Time Series Data
7. Plot Formatting
8. Statsmodels
9. Moving Averages (MA)
10. Simple Moving Average (SMA)
11. Exponentially Weighted Moving Average (EWMA)
12. Holt-Winters Methods
13. Forecasting
14. Stationarity
15. Differencing
    • First Order Differencing
    • Second order differencing
    • Lagging
16. Autocorrelation Function / Partial Autocorrelation Function
17. AR(p) - Autoregressive Model
18. Descriptive Statistics and Tests
19. Tests for Stationarity
    • Augmented Dickey-Fuller Test
    • Granger Causality Tests
20. Evaluating forecast accuracy
21. Choosing ARIMA Orders
22. Autoregressive Moving Average - ARMA(p,q)
23. Autoregressive Integrated Moving Average - ARIMA(p,d,q)
24. SARIMA(p,d,q)(P,D,Q)m
25. SARIMAX
26. VAR(p) - Vector Autoregression
27. VARMA(p,q) - Vector Autoregressive Moving Average
28. Keras Basics
29. Time Series Generator
30. Saving and Loading Models
31. Facebook's Prophet Basics