Time Series Forecasting in R: Modeling Techniques and Evaluation

btd
3 min readNov 18, 2023

Time series forecasting with R involves predicting future values based on past observations in a chronological sequence. Here’s a comprehensive guide covering the modeling and evaluation aspects:

1. Understanding Time Series Data:

  • Overview: Time series data is sequential, where observations occur over time. Understand the components of time series data: trend, seasonality, and noise.
# Load time series data
my_ts <- ts(data, frequency = 12) # Adjust frequency based on data

2. Exploratory Data Analysis (EDA):

  • Overview: Explore patterns, trends, and seasonality in the time series data before modeling.
# Plot time series data
plot(my_ts)

3. Time Series Modeling:

  • ARIMA (AutoRegressive Integrated Moving Average): ARIMA is a popular time series model that combines autoregression, differencing, and moving averages.
# Fit ARIMA model
arima_model <- arima(my_ts, order = c(p, d, q))
  • Exponential Smoothing State Space Models (ETS): ETS models capture seasonality, trend, and error components in a time series.
# Fit ETS model
ets_model <- ets(my_ts)
  • Prophet: Prophet is a forecasting tool designed for time series with daily observations that may contain missing data.
# Fit Prophet model
prophet_model <- prophet(my_df)

4. Model Evaluation:

  • Train-Test Split: Split the time series data into training and testing sets for model evaluation.
# Train-test split
train_size <- 0.8
train_data <- window(my_ts, end = time(my_ts)[round(length(my_ts) * train_size)])
test_data <- window(my_ts, start = time(my_ts)[round(length(my_ts) * train_size) + 1])
  • Forecasting and Evaluation: Generate forecasts and evaluate model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
# Generate forecasts
forecast_values <- forecast(arima_model, h = length(test_data))

# Evaluate forecast…

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