Time Series Analysis
Forecast time series data to understand trends and relationships. Provide your data file, load and display the first few rows, and specify the column containing the time series data. Parse this column as a datetime object, set it as the index, and visualize the data. Specify and apply any preprocessing steps needed, such as handling missing values or resampling. Decompose the time series into trend, seasonality, and residuals, perform stationarity tests, and specify the type of model to fit (e.g., ARIMA, SARIMA, Exponential Smoothing). Fit the model, display the summary, and generate and plot forecasts based on the fitted model. This helps in making informed decisions by analyzing and predicting future trends.
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Overview
Forecast time series data to understand trends and relationships. Provide your data file, load and display the first few rows, and specify the column containing the time series data. Parse this column as a datetime object, set it as the index, and visualize the data. Specify and apply any preprocessing steps needed, such as handling missing values or resampling. Decompose the time series into trend, seasonality, and residuals, perform stationarity tests, and specify the type of model to fit (e.g., ARIMA, SARIMA, Exponential Smoothing). Fit the model, display the summary, and generate and plot forecasts based on the fitted model. This helps in making informed decisions by analyzing and predicting future trends.
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