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tsforecast (version 1.1.0)

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')

Monthly Downloads

658

Version

1.1.0

License

GPL-3

Maintainer

Ka Karl Wu

Last Published

December 12th, 2025

Functions in tsforecast (1.1.0)

tsmltest

McLeod-Li Test for ARCH Effect
tsexplore

Explore a Time Series Numerically and Graphically
tsmodeleval

Goodness of Fit of a Time Series Model
tsforecast

Forecast Time Series based on Fitted Models
tsqqplot

Quantile-Quantile Plots
tslag

Lag a Time Series
tsesm

Exponential Smoothing Forecasts
tsmovav

Generate Moving Averages of a Time Series
tshistogram

Histograms
tslineplot

Time Series Line Plots
tsscatterplot

Scatter Plot
is.outlier

Outlier Identification
tsdiff

Difference a Time Series
tsconvert

Convert One-Dimensional Data to Time Series
airport

Airport Travellers Time Series.
tsarima

Fitting ARIMA Models
predict.tsarima

Predict Time Series Values
tsacf

Auto- Covariance and -Correlation Function Estimation
tsboxplot

Box Plots
ts-functions

Extract Information of a Time Series
tsdecomp

Decompose a Time Series