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|a Pal, Dr. Avishek.
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| 245 |
1 |
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|a Practical Time-Series Analysis.
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| 260 |
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|a Birmingham, UNKNOWN :
|b Packt Publishing,
|c 2017.
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| 300 |
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|a 1 online resource (238)
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|a Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Time Series; Different types of data; Cross-sectional data; Time series data; Panel data; Internal structures of time series; General trend; Seasonality; Run sequence plot; Seasonal sub series plot; Multiple box plots; Cyclical changes; Unexpected variations; Models for time series analysis; Zero mean models; Random walk; Trend models; Seasonality models; Autocorrelation and Partial autocorrelation; Summary.
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| 505 |
8 |
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|a Chapter 2: Understanding Time Series DataAdvanced processing and visualization of time series data; Resampling time series data; Group wise aggregation; Moving statistics; Stationary processes; Differencing; First-order differencing; Second-order differencing; Seasonal differencing; Augmented Dickey-Fuller test; Time series decomposition; Moving averages; Moving averages and their smoothing effect; Seasonal adjustment using moving average; Weighted moving average; Time series decomposition using moving averages; Time series decomposition using statsmodels.tsa; Summary.
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| 505 |
8 |
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|a Chapter 3: Exponential Smoothing based MethodsIntroduction to time-series smoothing; First order exponential smoothing; Second order exponential smoothing; Modeling higher-order exponential smoothing; Summary; Chapter 4: Auto-Regressive Models; Auto-regressive models; Moving average models; Building datasets with ARMA; ARIMA; Confidence interval; Summary; Chapter 5: Deep Learning for Time Series Forecasting; Multi-layer perceptrons; Training MLPs; MLPs for time series forecasting; Recurrent neural networks; Bi-directional recurrent neural networks; Deep recurrent neural networks.
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| 505 |
8 |
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|a Training recurrent neural networksSolving the long-range dependency problem; Long Short Term Memory; Gated Recurrent Units; Which one to use -- LSTM or GRU?; Recurrent neural networks for time series forecasting; Convolutional neural networks; 2D convolutions; 1D convolution; 1D convolution for time series forecasting; Summary; Chapter 6: Getting Started with Python; Installation; Python installers; Running the examples; Basic data types; List, tuple, and set; Strings; Maps; Keywords and functions; Iterators, iterables, and generators; Iterators; Iterables; Generators; Classes and objects.
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|a Time-series analysis.
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| 650 |
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|a Machine learning.
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6 |
|a Série chronologique.
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|a Apprentissage automatique.
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|x General.
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|a Machine learning
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