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tsforecast (version 1.3.0)

Time Series Forecasting Functions

Description

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) .

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Version

Install

install.packages('tsforecast')

Version

1.3.0

License

GPL-3

Maintainer

Ka Karl Wu

Last Published

January 21st, 2026

Functions in tsforecast (1.3.0)

airport

Airport Travellers Time Series.
tsdecomp

Decompose a Time Series
predict.tsarima

Predict Time Series Values
tsacf

Auto- Covariance and -Correlation Function Estimation
tsarima

Fitting ARIMA Models
tshistogram

Histograms
tsforecast

Forecast Time Series based on Fitted Models
tsdiff

Difference a Time Series
tslm

Generate Time Series Regression Model
tsmltest

McLeod-Li Test for ARCH Effect
tsconvert

Convert One-Dimensional Data to Time Series
tsboxplot

Box Plots
tsmodeleval

Goodness of Fit of a Time Series Model
tsmovav

Generate Moving Averages of a Time Series
ts-functions

Extract Information of a Time Series
tslag

Lag a Time Series
tslineplot

Time Series Line Plots
tsscatterplot

Scatter Plot
tsqqplot

Quantile-Quantile Plots
tsesm

Exponential Smoothing Forecasts
tsexplore

Explore a Time Series Numerically and Graphically
is.outlier

Outlier Identification