## 1 Introduction  
A time series is a set of records collected over time. To be called a time series, the data should be ordered chronologically, with equal distance between observation points. A weekly sale of a particular item in a store would comprise a time series. Depending on circumstances, data may be collected hourly, daily, weekly, monthly, etc. Time-indexed data could be the subject matter of a diverse spectrum of research areas, including science, engineering, and finance. Such broad interest from different fields has led to the development of an assorted set of forecasting approaches. Developing forecast models for time series can be challenging from several perspectives. In many instances, there is no insight into the underlying mechanism that generates data, hindering the development of a valid mathematical model. Empirical data are a product of a complex real-world mechanism (e.g., daily change in stock prices); standard statistical procedures may be inadequate to reveal the underlying relationships. That built-up complexity in the data may exhibit itself as noise or be observed as nonstationary or nonlinear behavior. Noise is a natural phenomenon because many undetected variables impact the controlled variable. For effective modeling, the noise has to be appropriately identified and excluded by the model. Nonstationarity refers to the dynamic nature of the data. The underlying process that generates the data changes over time, and the model has to be capable of determining these changes. In the long run, almost all data series become nonstationary because of the dynamic nature of the underlying processes. This brings up the issue of determining the length of usable history, which is a challenging task. To further aggravate the issue, the noise in the series may vary with time. Nonlinearity is also another interesting issue. Most of the time, models are based on the assumption that the underlying relationship is linear, which may not be valid for all instances. There are many examples where it has been shown that the nonlinear model serves as a better fit for the data [1]. Inherent difficulties in time series modeling led researchers to look for creative alternatives. One popular method is to combine different forecasting models in a framework to enhance forecasting capability. This process is better known as hybridization, based on the following premise. Each modeling methodology has its superior aspects but may be weak in others, and it is possible to combine them in a framework to maximize the overall benefit. Efforts to combine multiple forecasting models can be traced back to the pioneering work of [PERSON] and [PERSON] [2], in which two separate forecasts generated by different approaches are combined into one to minimize the variance. Many researchers followed the path opened by [PERSON] and [PERSON] [2] to develop creative hybrid models. With more accurate and reliable forecasts, hybrid models ranked top in prominent forecasting competitions. In a recent M4 competition, where each entrant had to generate forecasts for thousands of data sets, a hybrid model that combined exponential smoothing and recurrent neural networks came first by a solid margin [3]. The organizer of the M-competition, Prof. [PERSON], has the following comment about the hybrid models, which highlights the performance of hybrid models. _\"The accuracy when various methods are being combined outperforms, on average, the individual methods being combined and does very well in comparison to other methods\"_[4]. Our research contributes to this field with its unique structure of combining two standard forecasting methods. The rest of the paper is organized as follows. In Section 2, we present a literature review focusing on previous work directly related to our research. In Section 3, we present methodology, including a brief mention of ARIMA models and ANN. Our approach and modeling details are given in this section. In Section 4, the performance of our model with respect to benchmarks is presented in detail.