Time Series Forecasting Functions
Outlier Identification
Predict Time Series Values
Extract Information of a Time Series
Auto- Covariance and -Correlation Function Estimation
Fitting ARIMA Models
Box Plots
Convert One-Dimensional Data to Time Series
Decompose a Time Series
Difference a Time Series
Exponential Smoothing Forecasts
Explore a Time Series Numerically and Graphically
Forecast Time Series based on Fitted Models
Histograms
Lag a Time Series
Time Series Line Plots
Generate Time Series Regression Model
McLeod-Li Test for ARCH Effect
Goodness of Fit of a Time Series Model
Generate Moving Averages of a Time Series
Quantile-Quantile Plots
Scatter Plot
Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.