In machine learning, we have seen various kinds of neural networks and encoder-decoder models are also a type of neural network in which recurrent …

This article was published as a part of the Data Science Blogathon. One major problem we see every day include examining a situation over time. Time series forecast is extensively used in various scenarios like sales, weather, prices, etc…, where the underlying values of concern are a range of data points estimated over a period of time. This article strives to provide the essential structure of some of the algorithms for solving these classes of problems. We will explore various methods for time series forecasts.

Time series models predominantly, over the years, have focussed on individual time series via local models. This changed with the popularisation of deep learning techniques. This was also supported by the increase of temporal data availability, which led to many deep learning-based time series algorithms. Due to their natural temporal ordering, time-series data are present in almost every task that is registered, taking into account some notion of ordering. From electronic health records and human activity recognition to acoustic scene classification and cyber-security, time series is encountered in many real-world applications.

Any time series classification or regression forecasting involves the Y prediction at't n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence "we cannot make use future data in training" sounds too obvious and simple in theory, anyone unknowingly can add look ahead bias in complex forecasting problems. The discussion becomes important when you put in so much efforts in researching and building the model only to realize later that the back testing framework was using future data. It will also cost the data scientist a lot when the model is approved by Top Management and at the time of deploying the model realizing that we don't have the future data.

Any time series classification or regression forecasting involves the Y prediction at't n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence "we cannot make use future data in training" sounds too obvious and simple in theory, anyone unknowingly can add look ahead bias in complex forecasting problems. The discussion becomes important when you put in so much efforts in researching and building the model only to realize later that the back testing framework was using future data. It will also cost the data scientist a lot when the model is approved by Top Management and at the time of deploying the model realizing that we don't have the future data.