fforecast | R Documentation |

This function performs functional singular spectrum analysis (FSSA) recurrent forecasting (FSSA R-forecasting) or vector forecasting (FSSA V-forecasting) of univariate or multivariate functional time series (`fts`

) observed over a one-dimensional domain.

fforecast(U, groups = list(c(1)), h = 1, method = "recurrent", tol = 10^-3)

`U` |
An object of class |

`groups` |
A list of numeric vectors where each vector includes indices of elementary components of a group used for reconstruction and forecasting. |

`h` |
An integer that specifies the forecast horizon. |

`method` |
A character string specifying the type of forecasting to perform either |

`tol` |
A double specifying the amount of tolerated error in the approximation of the matrix that corresponds with the operator formed using a Neumann series leveraged in both forecasting algorithms; see Trinka et. al. (2021) for more details. |

A list of objects of class `fts`

where each fts corresponds to a forecasted group.

## Not run: # FSSA Forecasting require(Rfssa) load_github_data("https://github.com/haghbinh/Rfssa/blob/master/data/Callcenter.RData") ## Define functional objects D <- matrix(sqrt(Callcenter$calls), nrow = 240) N <- ncol(D) time <- substr(seq(ISOdate(1999, 1, 1), ISOdate(1999, 12, 31), by = "day"),1,10) K <- nrow(D) u <- seq(0, K, length.out = K) d <- 22 ## Define functional time series Y <- Rfssa::fts(list(D), list(list(d, "bspline")), list(u),time) plot(Y, mains = c("Call Center Data Line Plot"), xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) ## Perform FSSA decomposition L <- 28 U <- fssa(Y, L) groups <- list(1, 2:3, 4:5, 6:7, 1:7) ## Perform FSSA R-forecast and FSSA V-forecast pr_R <- fforecast(U = U, groups = groups, h = 30, method = "recurrent", tol = 10^-3) plot(pr_R[[1]], mains = "Call Center Mean Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_R[[2]], mains = "Call Center First Periodic Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_R[[3]], mains = "Call Center Second Periodic Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_R[[4]], mains = "Call Center Third Periodic Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(Y, mains = c("Call Center Data Line Plot"), xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_R[[5]], mains = "Call Center Extracted Signal Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) pr_V <- fforecast(U = U, groups = groups, h = 30, method = "vector", tol = 10^-3) plot(pr_V[[1]], mains = "Call Center Mean Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_V[[2]], mains = "Call Center First Periodic Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_V[[3]], mains = "Call Center Second Periodic Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_V[[4]], mains = "Call Center Third Periodic Component Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(Y, mains = c("Call Center Data Line Plot"), xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) plot(pr_V[[5]], mains = "Call Center Extracted Signal Forecast", xlabels = "Time (6 minutes aggregated)", ylabels = "Sqrt of Call Numbers",type="line", xticklabels = list(c("00:00","06:00","12:00","18:00","24:00")),xticklocs = list(c(1,60,120,180,240))) # MFSSA Forecasting require(Rfssa) load_github_data("https://github.com/haghbinh/Rfssa/blob/master/data/Jambi.RData") ## Raw image data NDVI <- Jambi$NDVI EVI <- Jambi$EVI time <- Jambi$Date ## Kernel density estimation of pixel intensity D0_NDVI <- matrix(NA, nrow = 512, ncol = 448) D0_EVI <- matrix(NA, nrow = 512, ncol = 448) for (i in 1:448) { D0_NDVI[, i] <- density(NDVI[, , i], from = 0, to = 1)$y D0_EVI[, i] <- density(EVI[, , i], from = 0, to = 1)$y } ## Define functional objects d <- 11 D <- list(D0_NDVI, D0_EVI) B <- list(list(d, "bspline"),list(d + 4, "fourier")) U <- list(c(0, 1), c(0, 1)) Y <- Rfssa::fts(D, B, U,time) plot(Y,mains = c("NDVI","EVI"),xlabels = c("NDVI","EVI"), ylabels = c("NDVI Density","EVI Density"),type="line", xticklabels = list(c("0","1"),c("0","1")),xticklocs = list(c(0,512),c(0,512))) U <- fssa(Y = Y, L = 45) groups <- list(c(1:4), c(1), c(2:3), c(4)) pr_R <- fforecast(U = U, groups = groups, h = 1, method = "recurrent") plot(pr_R[[1]]) plot(pr_R[[2]]) plot(pr_R[[3]]) plot(pr_R[[4]]) pr_V <- fforecast(U = U, groups = groups, h = 1, method = "vector") plot(pr_V[[1]]) plot(pr_V[[2]]) plot(pr_V[[3]]) plot(pr_V[[4]]) ## End(Not run)

Rfssa documentation built on Sept. 9, 2022, 1:07 a.m.

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