Data Scientist, SwissRe
Dev Advocate, Pinecone and Freelance ML
Chief Scientific Officer, Machine Medicine Technologies
Big Data & Analytics architect, Amazon
Deep Learning for Time Series Forecasting in Python -A Hands-On Approach to Build Deep Learning Models (MLP, CNN, LSTM, and a Hybrid Model CNN-LSTM) on Time Series Data.
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Business Objective
Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results While in classical Machine Learning models - such as autoregressive models (AR) or exponential smoothing - feature engineering is performed manually and often some parameters are optimized also considering the domain knowledge, Deep Learning models learn features and dynamics only and directly from the data. Thanks to this, they speed up the process of data preparation and are able to learn more complex data patterns in a more complete way.
Till now, in this sequence of time-series projects, we have covered the machine learning topics such as Autoregression modelling, Moving Average Smoothing techniques, ARIMA model, Multiple linear regression, Gaussian process, and ARCH- GARCH models.
In this project, we will demonstrate how deep learning in time series can be used. There are four major models which will be built,
(This is the seventh project of our time series list of projects; you can refer to the previous project through this link: Build ARCH and GARCH Models in Time Series using Python)
The dataset is “Call-centres” data. This data is at month level wherein the calls are segregated at domain level as the call centre operates for various domains. There are also external regressors like no of channels and no of phone lines which essentially indicate the traffic prediction of the inhouse analyst and the resources available.
The total number of rows are 132 and number of columns are 8:
We aim to build four deep learning models such as MLP, CNN, LSTM, and a hybrid CNN-LSTM model on the given time series dataset.
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