---
product_id: 407641468
title: "Time Series Forecasting using Deep Learning"
brand: "ivan gridin"
price: "COP 166221"
currency: COP
in_stock: true
reviews_count: 6
url: https://www.desertcart.co/products/407641468-time-series-forecasting-using-deep-learning
store_origin: CO
region: Colombia
---

# Time Series Forecasting using Deep Learning

**Brand:** ivan gridin
**Price:** COP 166221
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Time Series Forecasting using Deep Learning by ivan gridin
- **How much does it cost?** COP 166221 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.co](https://www.desertcart.co/products/407641468-time-series-forecasting-using-deep-learning)

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- ivan gridin enthusiasts

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## Description

Time Series Forecasting using Deep Learning

## Images

![Time Series Forecasting using Deep Learning - Image 1](https://m.media-amazon.com/images/I/81CM5kOLswL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ 5.0 out of 5 stars







  
  
    Great Book!
  

*by D***P on Reviewed in India on 23 November 2023*

This books goes deep inside deep learningLots of useful and real world examples with great model designs!A must read for time series analysis

### ⭐⭐⭐⭐⭐ 







  
    Muy buen libro
  
  

*by S***E on Reviewed in Mexico on 11 April 2022*

Claro y preciso, sin complicaciones matemáticas.  Ilustra muy bien los conceptos clave. Considero que su didáctica es muy adecuada.Felicitaciones al autor.

### ⭐⭐⭐⭐ 







  
  
    Very Good with Clean Practical Code
  

*by A***K on Reviewed in the United Kingdom on 19 February 2023*

Very good concise starting point for time series. It’s light on theory but the author makes this very clear in the introduction. This is a non-nonsense practical book, and assumes competence in python. The writing style is a bit terse in places but understandably so (given the author’s background), even so - the text is direct and clear. Liked that the author goes back to basics in code so that concepts are not just assumed or obscured by package calls (e.g., tensors, trend removal, alternate models). The more involved model descriptions (e.g., RNN/LSTN/TCN), can be a quite brief, you need to read between the lines, look at other sources, and work through the code.There is less coverage on more advanced topics, confidence intervals, regular and irregular timestamps, complex correlations, optimisations. The author could have cited deeper material and summarised gaps between where this code stops and real-world solutions start, That said, this is a solid starting point for time series. Another 100 pages would have really pushed this up a notch and been equally enjoyable to read. Nice one.

## Frequently Bought Together

- Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)
- Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
- Time Series Forecasting in Python

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*Product available on Desertcart Colombia*
*Store origin: CO*
*Last updated: 2026-05-02*