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12-month-forecast-with-lstm

This is an example of LSTM used to forecast the next 12 months on the Air Passengers dataset.

an-introduction-to-statistical-learning

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

atlas

Repository supporting the implementation of FAIR principles in the IPCC-WGI Atlas

beginning-data-science-with-jupyter

Packt courseware source code for "Beginning Data Science with Jupyter"

chapter-12

chapter-7

Figure and data generation for Chapter 7 of the IPCC's Sixth Assessment Report, Working Group 1 (plus assorted other contributions)

cit_lstm_timeseries

LSTM Model for Electric Load Forecasting

colormaps

IPCC AR6 Colormaps

data-science-complete-tutorial

For extensive instructor led learning

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

data_science-_capstone

Applied Data Science Capstone

dataviz

A book covering the fundamentals of data visualization

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

donwload-era5

dviz.supp

Supporting materials for Claus Wilke's data visualization book

fim-js

Flood inundation map libraries with real-time USGS river-level data.

food

gr4j

GR4J rainfall runoff model implemented in Python

gr4j-1

introstatlearn

Exercises from 'Introduction to Statistical Learning with Applications in R' written in Python.

islr

Introduction to Statistical Learning

islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

kiwis_pie

Python library for querying WISKI via KiWIS (KISTERS Web Interoperability Solution)

learndatascience

Open Content for self-directed learning in data science

load_forecasting

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

machine-learning-foundation-case-study

melodist

MELODIST is an open-source toolbox written in Python for disaggregating daily meteorological time series to hourly time steps. It is licensed under GPLv3 (see license file). The software framework consists of disaggregation functions for each variable including temperature, humidity, precipitation, shortwave radiation, and wind speed. These functions can simply be called from a station object, which includes all relevant information about site characteristics. The data management of time series is handled using data frame objects as defined in the pandas package. In this way, input and output data can be easily prepared and processed. For instance, the pandas package is data i/o capable and includes functions to plot time series using the matplotlib library.

multivariate-time-series-forecasting

Multivariate Time Series Forecasting with LSTM in Keras adapted to my problem

pangeo_lstm_example

Example notebook, showing how to use LSTMs for rainfall-runoff modeling

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera