  Time Series Forecasting is at the core of many practical applications such as
sales forecasting for business, rainfall forecasting for agriculture and many
others. Though this problem has been extensively studied for years, it is still
considered a challenging problem due to complex and evolving nature of time
series data. Typical methods proposed for time series forecasting modeled
linear or non-linear dependencies between data observations. However it is a
generally accepted notion that no one method is universally effective for all
kinds of time series data. Attempts have been made to use dynamic and weighted
combination of heterogeneous and independent forecasting models and it has been
found to be a promising direction to tackle this problem. This method is based
on the assumption that different forecasters have different specialization and
varying performance for different distribution of data and weights are
dynamically assigned to multiple forecasters accordingly. However in many
practical time series data-set, the distribution of data slowly evolves with
time. We propose to employ a re-weighting based method to adjust the assigned
weights to various forecasters in order to account for such distribution-drift.
An exhaustive testing was performed against both real-world and synthesized
time-series. Experimental results show the competitiveness of the method in
comparison to state-of-the-art approaches for combining forecasters and
handling drift.
