Time Series Forecasting in R: Modeling Techniques and Evaluation

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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…