Time series analysis is a statistical technique used to analyze data points collected over time. It's widely applied in finance, economics, weather forecasting, and more. Let's dive into the essentials!

Key Steps in Time Series Analysis

  1. Data Collection
    Gather historical data with timestamps. Example: stock prices or weather measurements.

    Time_Series_Data

  2. Data Preprocessing

    • Check for missing values (na.omit())
    • Convert to a time series object using ts() or tibble::as_tsibble()
    • Normalize or scale data if needed

  3. Exploratory Analysis
    Use plot() to visualize trends, seasonality, and outliers.

    Time_Series_Plots

  4. Modeling & Forecasting
    Common approaches include:

    • ARIMA models (forecast::auto.arima())
    • Exponential smoothing (stats::ets())
    • Machine learning (e.g., prophet for trend+seasonality)

  5. Evaluation
    Compare predictions with actual values using metrics like MAE or RMSE.

Recommended Packages

  • tsibble for tidy time series workflows
  • forecast for advanced forecasting methods
  • tseries for financial time series analysis
  • ggplot2 for visualization

Example Code

# Load data
data <- read.csv("sales_data.csv")
data$date <- as.Date(data$date)
ts_data <- tsibble(data, index = date)

# Plot time series
ts_data %>%
  ggplot(aes(x = date, y = sales)) +
  geom_line() +
  labs(title = "Monthly Sales Trend")

# Forecast using ARIMA
fit <- auto.arima(ts_data$sales)
forecast <- forecast(fit, h = 12)
autoplot(forecast) + labs(title = "ARIMA Forecast")

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Time_Series_Analysis_R