{ "_id": "6a228db0cd65a98ecbd54f73", "Package": "TSPred", "Type": "Package", "Title": "Functions for Benchmarking Time Series Prediction", "Version": "5.1.1", "Date": "2025-06-10", "Authors@R": "c(person(c(\"Rebecca\", \"Pontes\"),\"Salles\", role = c(\"aut\", \"cre\",\"cph\"),email = \"rebeccapsalles@acm.org\", comment = \"CEFET/RJ\"),\nperson(\"Eduardo\",\"Ogasawara\", role = c(\"ths\"),email = \"eogasawara@ieee.org\", comment = \"CEFET/RJ\"))", "Author": "Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ), Eduardo\nOgasawara [ths] (CEFET/RJ)", "Maintainer": "Rebecca Pontes Salles ", "Description": "Functions for defining and conducting a time series\nprediction process including pre(post)processing,\ndecomposition, modelling, prediction and accuracy assessment.\nThe generated models and its yielded prediction errors can be\nused for benchmarking other time series prediction methods and\nfor creating a demand for the refinement of such methods. For\nthis purpose, benchmark data from prediction competitions may\nbe used.", "License": "GPL (>= 2)", "BugReports": "https://github.com/RebeccaSalles/TSPred/issues", "URL": "https://github.com/RebeccaSalles/TSPred/wiki", "RoxygenNote": "7.3.2", "LazyData": "true", "Encoding": "UTF-8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-05 08:44:20 UTC", "User": "root" }, "Config/pak/sysreqs": "libgsl0-dev libpng-dev python3", "Repository": "https://rebeccasalles.r-universe.dev", "Date/Publication": "2025-06-10 21:13:19 UTC", "RemoteUrl": "https://github.com/rebeccasalles/tspred", "RemoteRef": "HEAD", "RemoteSha": "75d706b876dfb60ff47ab142edee2a1946d639ba", "MD5sum": "6eae1ebc9db34c0fd2829a207e2a16d2", "_user": "rebeccasalles", "_type": "src", "_file": "TSPred_5.1.1.tar.gz", "_fileid": "209184f293f9674582c5aa87338783d5a0e69dafb7cbac78ced33708e097f620", "_filesize": 1196823, "_sha256": "209184f293f9674582c5aa87338783d5a0e69dafb7cbac78ced33708e097f620", "_created": "2026-06-05T08:44:20.000Z", "_published": "2026-06-05T08:49:52.524Z", "_distro": "noble", "_jobs": [ { "job": 79694415820, "time": 157, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7432536337" }, { "job": 79694415806, "time": 152, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7432534671" }, { "job": 79694415888, "time": 290, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7432571845" }, { "job": 79694415877, "time": 143, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7432525956" }, { "job": 79693887422, "time": 217, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7432485724" }, { "job": 79694415798, "time": 116, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7432523120" }, { "job": 79694415814, "time": 104, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7432519473" }, { "job": 79694415837, "time": 99, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7432518466" }, { "job": 79694415809, "time": 103, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7432519335" } ], "_buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/rebeccasalles/tspred", "_commit": { "id": "75d706b876dfb60ff47ab142edee2a1946d639ba", "author": "Rebecca Pontes Salles ", "committer": "Rebecca Pontes Salles ", "message": "doc update\n", "time": 1749589999 }, "_maintainer": { "name": "Rebecca Pontes Salles", "email": "rebeccapsalles@acm.org" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "forecast", "role": "Imports" }, { "package": "KFAS", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "MuMIn", "role": "Imports" }, { "package": "wavelets", "role": "Imports" }, { "package": "ModelMetrics", "role": "Imports" }, { "package": "RSNNS", "role": "Imports" }, { "package": "Rlibeemd", "role": "Imports" }, { "package": "e1071", "role": "Imports" }, { "package": "elmNNRcpp", "role": "Imports" }, { "package": "nnet", "role": "Imports" }, { "package": "randomForest", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "plyr", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "keras", "role": "Imports" }, { "package": "tfdatasets", "role": "Imports" } ], "_owner": "rebeccasalles", "_selfowned": true, "_usedby": 1, "_updates": [ { "week": "2025-24", "n": 2 } ], "_tags": [], "_topics": [ "benchmarking", "linear-models", "machine-learning", "nonstationarity", "time-series-forecast", "time-series-prediction" ], "_stars": 27, "_contributors": [ { "user": "rebeccasalles", "count": 157, "uuid": 14221424 }, { "user": "eogasawara", "count": 1, "uuid": 12518421 } ], "_userbio": { "uuid": 14221424, "type": "user", "name": "Rebecca Pontes Salles" }, "_downloads": { "count": 700, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/TSPred" }, "_devurl": "https://github.com/rebeccasalles/tspred", "_searchresults": 103, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/readme.html", "extra/readme.md", "extra/TSPred.html", "manual.pdf" ], "_homeurl": "https://github.com/rebeccasalles/tspred", "_realowner": "rebeccasalles", "_cranurl": true, "_releases": [ { "version": "1.0", "date": "2015-03-30" }, { "version": "2.0", "date": "2015-04-07" }, { "version": "3.0", "date": "2017-03-17" }, { "version": "3.0.1", "date": "2017-03-25" }, { "version": "3.0.2", "date": "2017-04-05" }, { "version": "4.0", "date": "2018-06-21" }, { "version": "5.0", "date": "2021-01-09" }, { "version": "5.1", "date": "2021-01-21" }, { "version": "5.1.1", "date": "2025-06-10" } ], "_exports": [ "%>%", "AIC_eval", "AICc_eval", "an", "AN", "an.rev", "ARIMA", "arimainterp", "arimapar", "arimaparameters", "arimapred", "BCT", "BCT.rev", "benchmark", "BIC_eval", "BoxCoxT", "detrend", "detrend.rev", "DIF", "DIF.rev", "Diff", "DIFF", "Diff.rev", "ELM", "emd", "EMD", "emd.rev", "error", "ETS", "evaluate", "evaluating", "fitness", "fittestArima", "fittestArimaKF", "fittestEMD", "fittestLM", "fittestMAS", "fittestPolyR", "fittestPolyRKF", "fittestWavelet", "HW", "linear", "LogLik_eval", "LogT", "LogT.rev", "LT", "MAPE", "MAPE_eval", "marimapar", "marimapred", "mas", "MAS", "mas.rev", "MAXError", "MAXError_eval", "minmax", "MinMax", "minmax.rev", "MLM", "mlm_io", "MLP", "modeling", "MSE", "MSE_eval", "NAS", "NMSE", "NMSE_eval", "NNET", "outliers_bp", "pct", "PCT", "pct.rev", "plotarimapred", "postprocess", "preprocess", "processing", "RBF", "RFrst", "RMSE_eval", "slidingWindows", "sMAPE", "sMAPE_eval", "subset", "subsetting", "SVM", "sw", "SW", "Tensor_CNN", "Tensor_LSTM", "TF", "train", "train_test_subset", "tspred", "WaveletT", "WaveletT.rev", "workflow", "WT" ], "_datasets": [ { "name": "CATS", "title": "Time series of the CATS Competition", "object": "CATS", "class": [ "data.frame" ], "fields": [ "V1", "V2", "V3", "V4", "V5" ], "rows": 980, "table": true, "tojson": true }, { "name": "CATS.cont", "title": "Continuation dataset of the time series of the CATS Competition", "object": "CATS.cont", "class": [ "data.frame" ], "fields": [ "V1", "V2", "V3", "V4", "V5" ], "rows": 20, "table": true, "tojson": true }, { "name": "EUNITE.Loads", "title": "Electrical loads of the EUNITE Competition", "object": "EUNITE.Loads", "class": [ "data.frame" ], "fields": [ "X00.30", "X01.00", "X01.30", "X02.00", "X02.30", "X03.00", "X03.30", "X04.00", "X04.30", "X05.00", "X05.30", "X06.00", "X06.30", "X07.00", "X07.30", "X08.00", "X08.30", "X09.00", "X09.30", "X10.00", "X10.30", "X11.00", "X11.30", "X12.00", "X12.30", "X13.00", "X13.30", "X14.00", "X14.30", "X15.00", "X15.30", "X16.00", "X16.30", "X17.00", "X17.30", "X18.00", "X18.30", "X19.00", "X19.30", "X20.00", "X20.30", "X21.00", "X21.30", "X22.00", "X22.30", "X23.00", "X23.30", "X24.00" ], "rows": 730, "table": true, "tojson": true }, { "name": "EUNITE.Loads.cont", "title": "Continuation dataset of the electrical loads of the EUNITE Competition", "object": "EUNITE.Loads.cont", "class": [ "data.frame" ], "fields": [ "X00.30", "X01.00", "X01.30", "X02.00", "X02.30", "X03.00", "X03.30", "X04.00", "X04.30", "X05.00", "X05.30", "X06.00", "X06.30", "X07.00", "X07.30", "X08.00", "X08.30", "X09.00", "X09.30", "X10.00", "X10.30", "X11.00", "X11.30", "X12.00", "X12.30", "X13.00", "X13.30", "X14.00", "X14.30", "X15.00", "X15.30", "X16.00", "X16.30", "X17.00", "X17.30", "X18.00", "X18.30", "X19.00", "X19.30", "X20.00", "X20.30", "X21.00", "X21.30", "X22.00", "X22.30", "X23.00", "X23.30", "X24.00" ], "rows": 31, "table": true, "tojson": true }, { "name": "EUNITE.Reg", "title": "Electrical loads regressors of the EUNITE Competition", "object": "EUNITE.Reg", "class": [ "data.frame" ], "fields": [ "Holiday", "Weekday" ], "rows": 730, "table": true, "tojson": true }, { "name": "EUNITE.Reg.cont", "title": "Continuation dataset of the electrical loads regressors of the EUNITE Competition", "object": "EUNITE.Reg.cont", "class": [ "data.frame" ], "fields": [ "Holiday", "Weekday" ], "rows": 31, "table": true, "tojson": true }, { "name": "EUNITE.Temp", "title": "Temperatures of the EUNITE Competition", "object": "EUNITE.Temp", "class": [ "data.frame" ], "fields": [ "Temperature" ], "rows": 1461, "table": true, "tojson": true }, { "name": "EUNITE.Temp.cont", "title": "Continuation dataset of the temperatures of the EUNITE Competition", "object": "EUNITE.Temp.cont", "class": [ "data.frame" ], "fields": [ "Temperature" ], "rows": 31, "table": true, "tojson": true }, { "name": "ipeadata_d", "title": "The Ipea Most Requested Dataset (daily)", "object": "ipeadata_d", "class": [ "data.frame" ], "fields": [ "GM366_IBVSP366", "GM366_ERC366", "GM366_EREURO366", "GM366_ERPV366", "GM366_ERV366", "GM366_TJOVER366", "GM366_TJTR366", "SECEX366_MVTOT366", "SECEX366_XVTOT366", "JPM366_EMBI366", "BM366_TJOVER366", "GM366_TJOVERV366" ], "rows": 8154, "table": true, "tojson": true }, { "name": "ipeadata_d.cont", "title": "The Ipea Most Requested Dataset (daily)", "object": "ipeadata_d.cont", "class": [ "data.frame" ], "fields": [ "GM366_IBVSP366", "GM366_ERC366", "GM366_EREURO366", "GM366_ERPV366", "GM366_ERV366", "GM366_TJOVER366", "GM366_TJTR366", "SECEX366_MVTOT366", "SECEX366_XVTOT366", "JPM366_EMBI366", "BM366_TJOVER366", "GM366_TJOVERV366" ], "rows": 30, "table": true, "tojson": true }, { "name": "ipeadata_m", "title": "The Ipea Most Requested Dataset (monthly)", "object": "ipeadata_m", "class": [ "data.frame" ], "fields": [ "BM12_ERC12", "BM12_ERV12", "IGP12_IGPDI12", "FUNCEX12_MDPT12", "FUNCEX12_XPT12", "PRECOS12_INPC12", "PRECOS12_INPCBR12", "PRECOS12_IPCA12", "SEADE12_TDAGSP12", "SEADE12_TDOTSP12", "SEADE12_TDOPSP12", "GAC12_SALMINRE12", "IGP12_IGPM12", "PRECOS12_IPCAG12", "IGP12_IGPDIG12", "IGP12_IGPMG12", "IGP12_IGPOGG12", "PRECOS12_IPCA15G12", "BM12_PIB12", "MTE12_SALMIN12", "BM12_TJOVER12", "SEADE12_TDTGSP12", "PMEN12_TD12" ], "rows": 1019, "table": true, "tojson": true }, { "name": "ipeadata_m.cont", "title": "The Ipea Most Requested Dataset (monthly)", "object": "ipeadata_m.cont", "class": [ "data.frame" ], "fields": [ "BM12_ERC12", "BM12_ERV12", "IGP12_IGPDI12", "FUNCEX12_MDPT12", "FUNCEX12_XPT12", "PRECOS12_INPC12", "PRECOS12_INPCBR12", "PRECOS12_IPCA12", "SEADE12_TDAGSP12", "SEADE12_TDOTSP12", "SEADE12_TDOPSP12", "GAC12_SALMINRE12", "IGP12_IGPM12", "PRECOS12_IPCAG12", "IGP12_IGPDIG12", "IGP12_IGPMG12", "IGP12_IGPOGG12", "PRECOS12_IPCA15G12", "BM12_PIB12", "MTE12_SALMIN12", "BM12_TJOVER12", "SEADE12_TDTGSP12", "PMEN12_TD12" ], "rows": 12, "table": true, "tojson": true }, { "name": "NN3.A", "title": "Dataset A of the NN3 Competition", "object": "NN3.A", "class": [ "data.frame" ], "fields": [ "NN3.001", "NN3.002", "NN3.003", "NN3.004", "NN3.005", "NN3.006", "NN3.007", "NN3.008", "NN3.009", "NN3.010", "NN3.011", "NN3.012", "NN3.013", "NN3.014", "NN3.015", "NN3.016", "NN3.017", "NN3.018", "NN3.019", "NN3.020", "NN3.021", "NN3.022", "NN3.023", "NN3.024", "NN3.025", "NN3.026", "NN3.027", "NN3.028", "NN3.029", "NN3.030", "NN3.031", "NN3.032", "NN3.033", "NN3.034", "NN3.035", "NN3.036", "NN3.037", "NN3.038", "NN3.039", "NN3.040", "NN3.041", "NN3.042", "NN3.043", "NN3.044", "NN3.045", "NN3.046", "NN3.047", "NN3.048", "NN3.049", "NN3.050", "NN3.051", "NN3.052", "NN3.053", "NN3.054", "NN3.055", "NN3.056", "NN3.057", "NN3.058", "NN3.059", "NN3.060", "NN3.061", "NN3.062", "NN3.063", "NN3.064", "NN3.065", "NN3.066", "NN3.067", "NN3.068", "NN3.069", "NN3.070", "NN3.071", "NN3.072", "NN3.073", "NN3.074", "NN3.075", "NN3.076", "NN3.077", "NN3.078", "NN3.079", "NN3.080", "NN3.081", "NN3.082", "NN3.083", "NN3.084", "NN3.085", "NN3.086", "NN3.087", "NN3.088", "NN3.089", "NN3.090", "NN3.091", "NN3.092", "NN3.093", "NN3.094", "NN3.095", "NN3.096", "NN3.097", "NN3.098", "NN3.099", "NN3.100", "NN3_101", "NN3_102", "NN3_103", "NN3_104", "NN3_105", "NN3_106", "NN3_107", "NN3_108", "NN3_109", "NN3_110", "NN3_111" ], "rows": 126, "table": true, "tojson": true }, { "name": "NN3.A.cont", "title": "Continuation dataset of the Dataset A of the NN3 Competition", "object": "NN3.A.cont", "class": [ "data.frame" ], "fields": [ "NN3.001", "NN3.002", "NN3.003", "NN3.004", "NN3.005", "NN3.006", "NN3.007", "NN3.008", "NN3.009", "NN3.010", "NN3.011", "NN3.012", "NN3.013", "NN3.014", "NN3.015", "NN3.016", "NN3.017", "NN3.018", "NN3.019", "NN3.020", "NN3.021", "NN3.022", "NN3.023", "NN3.024", "NN3.025", "NN3.026", "NN3.027", "NN3.028", "NN3.029", "NN3.030", "NN3.031", "NN3.032", "NN3.033", "NN3.034", "NN3.035", "NN3.036", "NN3.037", "NN3.038", "NN3.039", "NN3.040", "NN3.041", "NN3.042", "NN3.043", "NN3.044", "NN3.045", "NN3.046", "NN3.047", "NN3.048", "NN3.049", "NN3.050", "NN3.051", "NN3.052", "NN3.053", "NN3.054", "NN3.055", "NN3.056", "NN3.057", "NN3.058", "NN3.059", "NN3.060", "NN3.061", "NN3.062", "NN3.063", "NN3.064", "NN3.065", "NN3.066", "NN3.067", "NN3.068", "NN3.069", "NN3.070", "NN3.071", "NN3.072", "NN3.073", "NN3.074", "NN3.075", "NN3.076", "NN3.077", "NN3.078", "NN3.079", "NN3.080", "NN3.081", "NN3.082", "NN3.083", "NN3.084", "NN3.085", "NN3.086", "NN3.087", "NN3.088", "NN3.089", "NN3.090", "NN3.091", "NN3.092", "NN3.093", "NN3.094", "NN3.095", "NN3.096", "NN3.097", "NN3.098", "NN3.099", "NN3.100", "NN3_101", "NN3_102", "NN3_103", "NN3_104", "NN3_105", "NN3_106", "NN3_107", "NN3_108", "NN3_109", "NN3_110", "NN3_111" ], "rows": 18, "table": true, "tojson": true }, { "name": "NN5.A", "title": "Dataset A of the NN5 Competition", "object": "NN5.A", "class": [ "data.frame" ], "fields": [ "NN5.001", "NN5.002", "NN5.003", "NN5.004", "NN5.005", "NN5.006", "NN5.007", "NN5.008", "NN5.009", "NN5.010", "NN5.011", "NN5.012", "NN5.013", "NN5.014", "NN5.015", "NN5.016", "NN5.017", "NN5.018", "NN5.019", "NN5.020", "NN5.021", "NN5.022", "NN5.023", "NN5.024", "NN5.025", "NN5.026", "NN5.027", "NN5.028", "NN5.029", "NN5.030", "NN5.031", "NN5.032", "NN5.033", "NN5.034", "NN5.035", "NN5.036", "NN5.037", "NN5.038", "NN5.039", "NN5.040", "NN5.041", "NN5.042", "NN5.043", "NN5.044", "NN5.045", "NN5.046", "NN5.047", "NN5.048", "NN5.049", "NN5.050", "NN5.051", "NN5.052", "NN5.053", "NN5.054", "NN5.055", "NN5.056", "NN5.057", "NN5.058", "NN5.059", "NN5.060", "NN5.061", "NN5.062", "NN5.063", "NN5.064", "NN5.065", "NN5.066", "NN5.067", "NN5.068", "NN5.069", "NN5.070", "NN5.071", "NN5.072", "NN5.073", "NN5.074", "NN5.075", "NN5.076", "NN5.077", "NN5.078", "NN5.079", "NN5.080", "NN5.081", "NN5.082", "NN5.083", "NN5.084", "NN5.085", "NN5.086", "NN5.087", "NN5.088", "NN5.089", "NN5.090", "NN5.091", "NN5.092", "NN5.093", "NN5.094", "NN5.095", "NN5.096", "NN5.097", "NN5.098", "NN5.099", "NN5.100", "NN5.101", "NN5.102", "NN5.103", "NN5.104", "NN5.105", "NN5.106", "NN5.107", "NN5.108", "NN5.109", "NN5.110", "NN5.111" ], "rows": 735, "table": true, "tojson": true }, { "name": "NN5.A.cont", "title": "Continuation dataset of the Dataset A of the NN5 Competition", "object": "NN5.A.cont", "class": [ "data.frame" ], "fields": [ "NN5.001", "NN5.002", "NN5.003", "NN5.004", "NN5.005", "NN5.006", "NN5.007", "NN5.008", "NN5.009", "NN5.010", "NN5.011", "NN5.012", "NN5.013", "NN5.014", "NN5.015", "NN5.016", "NN5.017", "NN5.018", "NN5.019", "NN5.020", "NN5.021", "NN5.022", "NN5.023", "NN5.024", "NN5.025", "NN5.026", "NN5.027", "NN5.028", "NN5.029", "NN5.030", "NN5.031", "NN5.032", "NN5.033", "NN5.034", "NN5.035", "NN5.036", "NN5.037", "NN5.038", "NN5.039", "NN5.040", "NN5.041", "NN5.042", "NN5.043", "NN5.044", "NN5.045", "NN5.046", "NN5.047", "NN5.048", "NN5.049", "NN5.050", "NN5.051", "NN5.052", "NN5.053", "NN5.054", "NN5.055", "NN5.056", "NN5.057", "NN5.058", "NN5.059", "NN5.060", "NN5.061", "NN5.062", "NN5.063", "NN5.064", "NN5.065", "NN5.066", "NN5.067", "NN5.068", "NN5.069", "NN5.070", "NN5.071", "NN5.072", "NN5.073", "NN5.074", "NN5.075", "NN5.076", "NN5.077", "NN5.078", "NN5.079", "NN5.080", "NN5.081", "NN5.082", "NN5.083", "NN5.084", "NN5.085", "NN5.086", "NN5.087", "NN5.088", "NN5.089", "NN5.090", "NN5.091", "NN5.092", "NN5.093", "NN5.094", "NN5.095", "NN5.096", "NN5.097", "NN5.098", "NN5.099", "NN5.100", "NN5.101", "NN5.102", "NN5.103", "NN5.104", "NN5.105", "NN5.106", "NN5.107", "NN5.108", "NN5.109", "NN5.110", "NN5.111" ], "rows": 56, "table": true, "tojson": true }, { "name": "SantaFe.A", "title": "Time series A of the Santa Fe Time Series Competition", "object": "SantaFe.A", "class": [ "data.frame" ], "fields": [ "V1" ], "rows": 1000, "table": true, "tojson": true }, { "name": "SantaFe.A.cont", "title": "Continuation dataset of the time series A of the Santa Fe Time Series Competition", "object": "SantaFe.A.cont", "class": [ "data.frame" ], "fields": [ "V1" ], "rows": 100, "table": true, "tojson": true }, { "name": "SantaFe.D", "title": "Time series D of the Santa Fe Time Series Competition", "object": "SantaFe.D", "class": [ "data.frame" ], "fields": [ "V1" ], "rows": 100000, "table": true, "tojson": true }, { "name": "SantaFe.D.cont", "title": "Continuation dataset of the time series D of the Santa Fe Time Series Competition", "object": "SantaFe.D.cont", "class": [ "data.frame" ], "fields": [ "V1" ], "rows": 500, "table": true, "tojson": true } ], "_help": [ { "page": "TSPred-package", "title": "Functions for Benchmarking Time Series Prediction", "topics": [ "TSPred-package" ] }, { "page": "an", "title": "Adaptive Normalization", "concept": [ "normalization methods" ], "topics": [ "an", "an.rev" ] }, { "page": "prediction_models", "title": "Time series prediction models", "concept": [ "constructors" ], "topics": [ "ARIMA", "ELM", "ETS", "HW", "MLP", "NNET", "RBF", "RFrst", "SVM", "Tensor_CNN", "Tensor_LSTM", "TF" ] }, { "page": "arimainterp", "title": "Interpolation of unknown values using automatic ARIMA fitting and prediction", "topics": [ "arimainterp" ] }, { "page": "arimaparameters", "title": "Get ARIMA model parameters", "topics": [ "arimaparameters" ] }, { "page": "arimapred", "title": "Automatic ARIMA fitting and prediction", "topics": [ "arimapred" ] }, { "page": "BCT", "title": "Box Cox Transformation", "topics": [ "BCT", "BCT.rev" ] }, { "page": "benchmark", "title": "Benchmarking a time series prediction process", "concept": [ "benchmark" ], "topics": [ "benchmark", "benchmark.tspred" ] }, { "page": "CATS", "title": "Time series of the CATS Competition", "topics": [ "CATS" ] }, { "page": "CATS.cont", "title": "Continuation dataset of the time series of the CATS Competition", "topics": [ "CATS.cont" ] }, { "page": "detrend", "title": "Detrending Transformation", "topics": [ "detrend", "detrend.rev" ] }, { "page": "Diff", "title": "Differencing Transformation", "concept": [ "transformation methods" ], "topics": [ "Diff", "Diff.rev" ] }, { "page": "emd", "title": "Automatic empirical mode decomposition", "concept": [ "transformation methods" ], "topics": [ "emd", "emd.rev" ] }, { "page": "EUNITE.Loads", "title": "Electrical loads of the EUNITE Competition", "topics": [ "EUNITE.Loads" ] }, { "page": "EUNITE.Loads.cont", "title": "Continuation dataset of the electrical loads of the EUNITE Competition", "topics": [ "EUNITE.Loads.cont" ] }, { "page": "EUNITE.Reg", "title": "Electrical loads regressors of the EUNITE Competition", "topics": [ "EUNITE.Reg" ] }, { "page": "EUNITE.Reg.cont", "title": "Continuation dataset of the electrical loads regressors of the EUNITE Competition", "topics": [ "EUNITE.Reg.cont" ] }, { "page": "EUNITE.Temp", "title": "Temperatures of the EUNITE Competition", "topics": [ "EUNITE.Temp" ] }, { "page": "EUNITE.Temp.cont", "title": "Continuation dataset of the temperatures of the EUNITE Competition", "topics": [ "EUNITE.Temp.cont" ] }, { "page": "evaluate", "title": "Evaluating prediction/modeling quality", "concept": [ "evaluate" ], "topics": [ "evaluate", "evaluate.error", "evaluate.evaluating", "evaluate.fitness" ] }, { "page": "evaluate.tspred", "title": "Evaluate method for 'tspred' objects", "concept": [ "evaluate" ], "topics": [ "evaluate.tspred" ] }, { "page": "evaluating", "title": "Prediction/modeling quality evaluation", "concept": [ "constructors" ], "topics": [ "error", "evaluating", "fitness" ] }, { "page": "fittestArima", "title": "Automatic ARIMA fitting, prediction and accuracy evaluation", "topics": [ "fittestArima" ] }, { "page": "fittestArimaKF", "title": "Automatic ARIMA fitting and prediction with Kalman filter", "topics": [ "fittestArimaKF" ] }, { "page": "fittestEMD", "title": "Automatic prediction with empirical mode decomposition", "topics": [ "fittestEMD" ] }, { "page": "fittestLM", "title": "Automatically finding fittest linear model for prediction", "topics": [ "fittestLM" ] }, { "page": "fittestMAS", "title": "Automatic prediction with moving average smoothing", "topics": [ "fittestMAS" ] }, { "page": "fittestPolyR", "title": "Automatic fitting and prediction of polynomial regression", "topics": [ "fittestPolyR" ] }, { "page": "fittestPolyRKF", "title": "Automatic fitting and prediction of polynomial regression with Kalman filter", "topics": [ "fittestPolyRKF" ] }, { "page": "fittestWavelet", "title": "Automatic prediction with wavelet transform", "topics": [ "fittestWavelet" ] }, { "page": "ipeadata_d", "title": "The Ipea Most Requested Dataset (daily)", "topics": [ "ipeadata_d", "ipeadata_d.cont" ] }, { "page": "ipeadata_m", "title": "The Ipea Most Requested Dataset (monthly)", "topics": [ "ipeadata_m", "ipeadata_m.cont" ] }, { "page": "LogT", "title": "Logarithmic Transformation", "concept": [ "transformation methods" ], "topics": [ "LogT", "LogT.rev" ] }, { "page": "transformation_methods", "title": "Time series transformation methods", "concept": [ "constructors" ], "topics": [ "AN", "BoxCoxT", "DIFF", "EMD", "LT", "MAS", "MinMax", "NAS", "PCT", "subsetting", "SW", "WT" ] }, { "page": "MAPE", "title": "MAPE error of prediction", "topics": [ "MAPE" ] }, { "page": "marimapar", "title": "Get parameters of multiple ARIMA models.", "topics": [ "marimapar" ] }, { "page": "marimapred", "title": "Multiple time series automatic ARIMA fitting and prediction", "topics": [ "marimapred" ] }, { "page": "MAS", "title": "Moving average smoothing", "concept": [ "transformation methods" ], "topics": [ "mas", "mas.rev" ] }, { "page": "MAXError", "title": "Maximal error of prediction", "topics": [ "MAXError" ] }, { "page": "minmax", "title": "Minmax Data Normalization", "concept": [ "normalization methods" ], "topics": [ "minmax", "minmax.rev" ] }, { "page": "modeling", "title": "Time series modeling and prediction", "concept": [ "constructors" ], "topics": [ "linear", "MLM", "modeling" ] }, { "page": "MSE", "title": "MSE error of prediction", "topics": [ "MSE" ] }, { "page": "quality_metrics", "title": "Prediction/modeling quality metrics", "concept": [ "constructors" ], "topics": [ "AICc_eval", "AIC_eval", "BIC_eval", "LogLik_eval", "MAPE_eval", "MAXError_eval", "MSE_eval", "NMSE_eval", "RMSE_eval", "sMAPE_eval" ] }, { "page": "NMSE", "title": "NMSE error of prediction", "topics": [ "NMSE" ] }, { "page": "NN3.A", "title": "Dataset A of the NN3 Competition", "topics": [ "NN3.A" ] }, { "page": "NN3.A.cont", "title": "Continuation dataset of the Dataset A of the NN3 Competition", "topics": [ "NN3.A.cont" ] }, { "page": "NN5.A", "title": "Dataset A of the NN5 Competition", "topics": [ "NN5.A" ] }, { "page": "NN5.A.cont", "title": "Continuation dataset of the Dataset A of the NN5 Competition", "topics": [ "NN5.A.cont" ] }, { "page": "outliers_bp", "title": "Outlier removal from sliding windows of data", "concept": [ "transformation methods" ], "topics": [ "outliers_bp" ] }, { "page": "PCT", "title": "Percentage Change Transformation", "concept": [ "transformation methods" ], "topics": [ "pct", "pct.rev" ] }, { "page": "plotarimapred", "title": "Plot ARIMA predictions against actual values", "topics": [ "plotarimapred" ] }, { "page": "postprocess.tspred", "title": "Postprocess method for 'tspred' objects", "concept": [ "preprocess" ], "topics": [ "postprocess.tspred" ] }, { "page": "predict", "title": "Predict method for 'modeling' objects", "concept": [ "predict" ], "topics": [ "predict", "predict.linear", "predict.MLM" ] }, { "page": "predict.tspred", "title": "Predict method for 'tspred' objects", "concept": [ "predict" ], "topics": [ "predict.tspred" ] }, { "page": "preprocess", "title": "Preprocessing/Postprocessing time series data", "concept": [ "processing" ], "topics": [ "postprocess", "postprocess.processing", "preprocess", "preprocess.processing" ] }, { "page": "preprocess.tspred", "title": "Preprocess method for 'tspred' objects", "concept": [ "preprocess" ], "topics": [ "preprocess.tspred" ] }, { "page": "processing", "title": "Time series data processing", "concept": [ "constructors" ], "topics": [ "processing" ] }, { "page": "SantaFe.A", "title": "Time series A of the Santa Fe Time Series Competition", "topics": [ "SantaFe.A" ] }, { "page": "SantaFe.A.cont", "title": "Continuation dataset of the time series A of the Santa Fe Time Series Competition", "topics": [ "SantaFe.A.cont" ] }, { "page": "SantaFe.D", "title": "Time series D of the Santa Fe Time Series Competition", "topics": [ "SantaFe.D" ] }, { "page": "SantaFe.D.cont", "title": "Continuation dataset of the time series D of the Santa Fe Time Series Competition", "topics": [ "SantaFe.D.cont" ] }, { "page": "sMAPE", "title": "sMAPE error of prediction", "topics": [ "sMAPE" ] }, { "page": "subset", "title": "Subsetting data into training and testing sets", "concept": [ "preprocess" ], "topics": [ "subset", "subset.tspred" ] }, { "page": "sw", "title": "Generating sliding windows of data", "topics": [ "sw" ] }, { "page": "train", "title": "Training a time series model", "concept": [ "train" ], "topics": [ "train", "train.linear", "train.MLM" ] }, { "page": "train_test_subset", "title": "Get training and testing subsets of data", "concept": [ "transformation methods" ], "topics": [ "train_test_subset" ] }, { "page": "train.tspred", "title": "Train method for 'tspred' objects", "concept": [ "train" ], "topics": [ "train.tspred" ] }, { "page": "tspred", "title": "Time series prediction process", "concept": [ "constructors" ], "topics": [ "tspred" ] }, { "page": "WaveletT", "title": "Automatic wavelet transform", "concept": [ "transformation methods" ], "topics": [ "WaveletT", "WaveletT.rev" ] }, { "page": "workflow", "title": "Executing a time series prediction process", "concept": [ "workflow" ], "topics": [ "workflow", "workflow.tspred" ] } ], "_readme": "https://github.com/rebeccasalles/tspred/raw/HEAD/README.md", "_rundeps": [ "backports", "base64enc", "class", "cli", "colorspace", "config", "cpp11", "data.table", "dplyr", "e1071", "elmNNRcpp", "farver", "forecast", "fracdiff", "generics", "ggplot2", "glue", "gtable", "here", "insight", "isoband", "jsonlite", "keras", "KernelKnn", "KFAS", "labeling", "lattice", "lifecycle", "lmtest", "magrittr", "MASS", "Matrix", "ModelMetrics", "MuMIn", "nlme", "nnet", "pillar", "pkgconfig", "plyr", "png", "processx", "proxy", "ps", "R6", "randomForest", "rappdirs", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppTOML", "reticulate", "rlang", "Rlibeemd", "rprojroot", "RSNNS", "rstudioapi", "S7", "scales", "tensorflow", "tfautograph", "tfdatasets", "tfruns", "tibble", "tidyselect", "timeDate", "urca", "utf8", "vctrs", "viridisLite", "wavelets", "whisker", "withr", "yaml", "zeallot", "zoo" ], "_score": 5.620292247919841, "_indexed": true, "_nocasepkg": "tspred", "_universes": [ "rebeccasalles" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "5.1.1", "date": "2026-06-05T08:46:48.000Z", "distro": "noble", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "c4e28964619920f09ae764d15e81d52516fd48e6f29178c61caab5b6df03731c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.6.0", "os": "linux", "version": "5.1.1", "date": "2026-06-05T08:46:48.000Z", "distro": "noble", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "a276412d1dfc151677b98c101c8baa29063c086b560f9fa3d364dacccf2a438b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.5.3", "os": "mac", "version": "5.1.1", "date": "2026-06-05T08:46:25.000Z", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "e623242b7d0ddc4143ef7bd0924bc5ae804a98cc51226e95a6a98b7705ce5a1b", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.6.0", "os": "mac", "version": "5.1.1", "date": "2026-06-05T08:46:30.000Z", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "9bcdf876a51584f928a5fd248af76e2e7e0cea3477a3da175aa9c67d622cf8c8", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.6.0", "os": "wasm", "version": "5.1.1", "date": "2026-06-05T08:46:42.000Z", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "d551f51636ddd3d0c089281c5635bff51769ad7032e76ae69214b2022ec9b878", "status": "success", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.7.0", "os": "win", "version": "5.1.1", "date": "2026-06-05T08:45:46.000Z", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "6d808b04082cf0ac3034f3db1be45ed4c4edf755e946048a0135b506da99b356", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.5.3", "os": "win", "version": "5.1.1", "date": "2026-06-05T08:45:53.000Z", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "955f417c966cb6b0e8156246eaaf9dc0efc07f76583ec8571e350d15bfbad5f1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" }, { "r": "4.6.0", "os": "win", "version": "5.1.1", "date": "2026-06-05T08:45:48.000Z", "commit": "75d706b876dfb60ff47ab142edee2a1946d639ba", "fileid": "b030630362c3743207e97e53c7107df72a84999d6bea9d7f39bebaa6dff728cc", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/rebeccasalles/actions/runs/27004861336" } ] }