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phi 3.5 mini and llama 3.1 added. Small improvements.
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@@ -47,3 +47,6 @@ po/*~ | |
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| # RStudio Connect folder | ||
| rsconnect/ | ||
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| # api file | ||
| api.R | ||
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| #### mode #### | ||
| getmode <- function(v, na = TRUE) { | ||
| if(na == TRUE){ | ||
| v <- v[!is.na(v)] | ||
| } | ||
| uniqv <- unique(v) | ||
| uniqv[which.max(tabulate(match(v, uniqv)))] | ||
| } | ||
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| #### Forecasting functions #### | ||
| #### LSTM #### | ||
| lstm_forecast <- function(ts_data, horizon) { | ||
| library(keras) | ||
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| # Normalize the data | ||
| normalized_data <- scale(ts_data) | ||
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| # Split data into train and test sets | ||
| train_data <- normalized_data[1:(length(normalized_data) - horizon)] | ||
| test_data <- normalized_data[(length(normalized_data) - horizon + 1):length(normalized_data)] | ||
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| # Prepare the training data | ||
| train_x <- train_y <- list() | ||
| for (i in 1:(length(train_data) - horizon)) { | ||
| train_x[[i]] <- matrix(train_data[i:(i + horizon - 1)], nrow = horizon, ncol = 1) | ||
| train_y[[i]] <- train_data[(i + horizon)] | ||
| } | ||
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| train_x <- array_reshape(train_x, c(length(train_x), horizon, 1)) | ||
| train_y <- unlist(train_y) | ||
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| # Define the LSTM model architecture | ||
| model <- keras_model_sequential() | ||
| model %>% | ||
| layer_lstm(units = 50, input_shape = c(horizon, 1)) %>% | ||
| layer_dense(units = 1) | ||
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| # Compile the model | ||
| model %>% compile( | ||
| loss = "mean_squared_error", | ||
| optimizer = optimizer_adam() | ||
| ) | ||
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| # Train the model | ||
| model %>% fit( | ||
| train_x, train_y, | ||
| epochs = 100, | ||
| batch_size = 32, | ||
| verbose = 0 | ||
| ) | ||
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| # Make predictions for the test set | ||
| test_x <- array_reshape(test_data, dim = c(length(test_data) / horizon, horizon, 1)) | ||
| predicted_values <- model %>% predict(test_x) | ||
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| # Denormalize the predicted values | ||
| denormalized_values <- predicted_values * sd(ts_data) + mean(ts_data) | ||
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| # Create the forecast object | ||
| forecast_values <- ts(denormalized_values, frequency = 12) | ||
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| return(forecast_values) | ||
| } | ||
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| #### AutoML #### | ||
| automl_forecast <- function(ts_data, horizon) { | ||
| # Convert the time series data to a data frame | ||
| data_df <- data.frame(Date = as.numeric(time(ts_data)), Value = as.numeric(ts_data)) | ||
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| # Initialize the H2O cluster | ||
| h2o.init() | ||
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| # Convert the data frame to an H2O frame | ||
| h2o_df <- as.h2o(data_df, destination_frame = "ts_data") | ||
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| # Set the target variable | ||
| target <- "Value" | ||
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| # Train AutoML model | ||
| aml <- h2o.automl(x = setdiff(colnames(h2o_df), target), | ||
| y = target, | ||
| training_frame = h2o_df, | ||
| max_runtime_secs = 300, | ||
| max_models = 10) | ||
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| # Generate predictions for the future horizon | ||
| forecast_df <- data.frame(Date = seq(time(ts_data)[length(ts_data)] + 1/12, by = 1/12, length.out = horizon)) | ||
| forecast_h2o <- as.h2o(forecast_df, destination_frame = "forecast_data") | ||
| forecast_predictions <- h2o.predict(aml@leader, forecast_h2o) | ||
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| # Convert the predictions to a time series object | ||
| forecast_values <- ts(as.vector(forecast_predictions$predict), frequency = 12) | ||
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| # Shut down the H2O cluster | ||
| h2o.shutdown(prompt = FALSE) | ||
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| return(forecast_values) | ||
| } | ||
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| #### ARFIMA Forecast #### | ||
| arfima_forecast <- function(x, h){ | ||
| { | ||
| arfima_fit <- auto.arima(x, seasonal = FALSE, stepwise = FALSE, | ||
| approximation = FALSE, allowdrift = FALSE) | ||
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| # Extract AR and MA orders from the fitted model | ||
| ar_order <- arimaorder(arfima_fit)[1] # AR order | ||
| ma_order <- arimaorder(arfima_fit)[3] # MA order | ||
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| # Create dynamic ARFIMA specification | ||
| spec <- arfimaspec(mean.model = list(armaOrder = c(ar_order, ma_order), | ||
| include.mean = TRUE, arfima = TRUE)) | ||
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| result <- try({ | ||
| # Fit the model using the dynamic specification | ||
| garch_fit <- arfimafit(spec = spec, data = x) | ||
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| # Generate forecasts directly using arfimaforecast | ||
| forecast_values <- arfimaforecast(garch_fit, n.ahead = h) | ||
| }, silent = TRUE) | ||
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| if (inherits(result, "try-error")) { | ||
| forecast_vector <- rep(0, times = h) | ||
| } else { | ||
| # Extract the forecasted values as a vector | ||
| forecast_vector <- as.vector(forecast_values@forecast$seriesFor) | ||
| } | ||
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| return(forecast_vector) | ||
| } | ||
| } |
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