PackageForecast package |
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forecast: Forecasting Functions for Time Series and Linear Models |
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Time series analysisFunctions for working with time series |
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Multi-Seasonal Time Series |
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Subsetting a time series |
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Number of trading days in each season |
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Easter holidays in each season |
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Number of days in each season |
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Fourier terms for modelling seasonality |
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Seasonal dummy variables |
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Find dominant frequency of a time series |
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Box Cox Transformation |
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Automatic selection of Box Cox transformation parameter |
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Identify and replace outliers and missing values in a time series |
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Identify and replace outliers in a time series |
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Interpolate missing values in a time series |
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Number of differences required for a stationary series |
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Number of differences required for a seasonally stationary series |
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Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots |
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Box-Cox and Loess-based decomposition bootstrap. |
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Is an object constant? |
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Seasonal decompositionFunctions used in seasonal decomposition |
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Moving-average smoothing |
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Multiple seasonal decomposition |
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Seasonal adjustment |
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Extract components from a time series decomposition |
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ModellingFunctions for estimating time series models |
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Fit a fractionally differenced ARFIMA model |
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Fit ARIMA model to univariate time series |
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Fit best ARIMA model to univariate time series |
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Exponential smoothing state space model |
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Forecasting using a bagged model |
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BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
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TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
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Neural Network Time Series Forecasts |
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Forecasting using stl objects |
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Fit a linear model with time series components |
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ForecastingFunctions for producing forecasts |
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Naive and Random Walk Forecasts |
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Mean Forecast |
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Exponential smoothing forecasts |
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Double-Seasonal Holt-Winters Forecasting |
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Forecasting using stl objects |
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Cubic Spline Forecast |
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Theta method forecast |
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Forecasts for intermittent demand using Croston's method |
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Time Series Forecasts with a user-defined model |
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Forecast seasonal index |
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Forecasting using ARIMA or ARFIMA models |
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Forecasting using ETS models |
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Forecasting using a bagged model |
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Forecasting using BATS and TBATS models |
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Forecasting using Holt-Winters objects |
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Forecast a linear model with possible time series components |
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Forecast a multiple linear model with possible time series components |
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Forecasting using user-defined model |
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Forecasting time series |
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Forecasting using neural network models |
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Forecasting using Structural Time Series models |
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Forecasting time series |
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Is an object a particular forecast type? |
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PlottingFunctions for plotting time series and forecasts |
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Histogram with optional normal and kernel density functions |
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Seasonal plot |
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Create a seasonal subseries ggplot |
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Time series lag ggplots |
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(Partial) Autocorrelation and Cross-Correlation Function Estimation |
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ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting |
Time series display |
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Check that residuals from a time series model look like white noise |
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Forecast plot |
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Plot characteristic roots from ARIMA model |
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Plot components from BATS model |
Plot components from ETS model |
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Forecast plot |
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Multivariate forecast plot |
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Plot time series decomposition components using ggplot |
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Automatically create a ggplot for time series objects |
Create a ggplot layer appropriate to a particular data type |
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Is an object a particular model type? |
Model analysisFunctions for analysing time series models |
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h-step in-sample forecasts for time series models. |
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Residuals for various time series models |
Check that residuals from a time series model look like white noise |
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Return the order of an ARIMA or ARFIMA model |
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Errors from a regression model with ARIMA errors |
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Simulation from a time series model |
Extract components of a TBATS model |
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Get response variable from time series model. |
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Forecast evaluationFunctions used for evaluating forecasts |
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Accuracy measures for a forecast model |
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k-fold Cross-Validation applied to an autoregressive model |
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Cross-validation statistic |
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Time series cross-validation |
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Diebold-Mariano test for predictive accuracy |
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DataData sets included in the package |
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Australian monthly gas production |
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Daily morning gold prices |
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Half-hourly electricity demand |
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Australian total wine sales |
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Quarterly production of woollen yarn in Australia |