# Automatic Decomposition Function Automatically decomposes a time series using singular spectrum analysis. See package Rssa for details on singular spectrum analysis. ```r auto_decompose( time_series, suspected_periods = c(12, 6, 4, 3), proportion_of_variance_type = c("leave_out_first", "total"), max_proportion_of_variance = 0.995, log_ratio_cutoff = 0.2, window_length = "auto", num_trend_components = 2 ) ``` ## Arguments - `time_series`: A vector which stores the time series of interest in the log scale. - `suspected_periods`: A vector which stores the suspected periods in the descending order of importance. The default option is c(12,6,4,3), corresponding to 12, 6, 4, and 3 months. - `proportion_of_variance_type`: A character string specifying the option for choosing the maximum number of eigenvalues based on the proportion of total variance explained. If "leave_out_first" is chosen, then the contribution made by the first eigenvector is ignored; otherwise, if "total" is chosen, then the contribution made by all the eigenvectors is considered. - `max_proportion_of_variance`: A numeric specifying the proportion of total variance explained using the method specified in proportion_of_variance_type. The default option is 0.995. - `log_ratio_cutoff`: A numeric specifying the threshold for the deviation between the estimated period and candidate periods in suspected_periods. The default option is 0.2, which means that, if the absolute log ratio between the estimated and candidate period is within 0.2 (approximately a 20% difference), then the estimated period is deemed equal to the candidate period. - `window_length`: A character string or positive integer specifying the window length for the SSA estimation. If "auto" is chosen, then the algorithm automatically selects the window length by taking a multiple of 12 which does not exceed half the length of time_series. The default option is "auto". - `num_trend_components`: A positive integer specifying the number of eigenvectors to be chosen for describing the trend in SSA. The default option is 2. ## Returns - **reconstruction**: A list containing important information about the reconstructed time series. In particular, it contains the reconstructed main trend component, overall trend component, seasonal component for each period specified in suspected_periods, and overall seasonal component. - **grouping**: A matrix containing information about the locations of the eigenvalue groups for each period in suspected_periods and trend component. The locations are indicated by '1'. - **window_length**: A numeric indicating the window length. - **ts_ssa**: An ssa object storing the singular spectrum analysis decomposition. ## Examples ```r data("park_visitation") ### Decompose national parks service visitor counts and flickr photo user-days # parameters --------------------------------------------- suspected_periods <- c(12,6,4,3) proportion_of_variance_type = "leave_out_first" max_proportion_of_variance <- 0.995 log_ratio_cutoff <- 0.2 # load data ---------------------------------------------- park <- "YELL" #for Yellowstone National Park nps_ts <- ts(park_visitation[park_visitation$park == park,]$nps, start = 2005, freq = 12) nps_ts <- log(nps_ts) pud_ts <- ts(park_visitation[park_visitation$park == park,]$pud, start = 2005, freq = 12) pud_ts <- log(pud_ts) # decompose time series and plot decompositions ----------- decomp_pud <- auto_decompose(pud_ts, suspected_periods, proportion_of_variance_type = proportion_of_variance_type, max_proportion_of_variance, log_ratio_cutoff) plot(decomp_pud) decomp_nps <- auto_decompose(nps_ts,suspected_periods, proportion_of_variance_type = proportion_of_variance_type, max_proportion_of_variance,log_ratio_cutoff) plot(decomp_nps) ```

auto_decompose function