How can we fill in the gaps in a time series data?

edit

There are several methods that can be used to fill gaps in a time series data:

1. Interpolation: This involves using the neighboring data points to estimate the missing values. Linear interpolation, cubic spline interpolation, and other methods are commonly used.

2. Forward fill or backward fill: This involves using the last known value to fill in the missing data point (forward fill) or using the next known value to fill in the missing data point (backward fill).

3. Seasonal decomposition: This method breaks down the time series data into its seasonal, trend, and residual components and then predicts the missing values based on the identified patterns.

4. Forecasting models: Time series forecasting models such as ARIMA, exponential smoothing, and neural networks can be used to predict the missing values.

5. Imputation: This involves using statistical methods to estimate missing values based on the distribution of available data. Techniques such as mean imputation, median imputation, and multiple imputations can be used.