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time_series_tutorial.txt
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Sampling of time-series data in Pandas:
https://stackoverflow.com/questions/27823273/counting-frequency-of-values-by-date-using-pandas
IMBALANCE CLASS TREATMENT & MOdelling:
https://towardsdatascience.com/comparing-different-classification-machine-learning-models-for-an-imbalanced-dataset-fdae1af3677f
http://benalexkeen.com/resampling-time-series-data-with-pandas/
https://machinelearningmastery.com/resample-interpolate-time-series-data-python/
https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html
Time-Series Modeling:
https://towardsdatascience.com/playing-with-time-series-data-in-python-959e2485bff8
Understanding LSTM:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Complete Guide:
https://www.youtube.com/watch?v=e8Yw4alG16Q
Good Example for Moving Average Smoothing:
https://machinelearningmastery.com/moving-average-smoothing-for-time-series-forecasting-python/
Good Explanation:
https://towardsdatascience.com/trend-seasonality-moving-average-auto-regressive-model-my-journey-to-time-series-data-with-edc4c0c8284b
A very clear explanation of the Modeling components:
https://newonlinecourses.science.psu.edu/stat510/node/47/
Addtive vs Multiplicative Trend: --- Graphical representation
http://www.simafore.com/blog/bid/205420/Time-series-forecasting-understanding-trend-and-seasonality
Good Idea on possibilities of Approaches for Time-Series Prediction Models:
https://www.analyticsvidhya.com/blog/2018/08/auto-arima-time-series-modeling-python-r/
Optmial P,D, Q Values:
https://stackoverflow.com/questions/22770352/auto-arima-equivalent-for-python
Very Good example for Time-Series end-to-end:
https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b
Good Observation of De-Trending by Difference & Division:
https://anomaly.io/seasonal-trend-decomposition-in-r/
Good Explanation of ARIMA:
https://www.analyticsvidhya.com/blog/2015/12/complete-tutorial-time-series-modeling/
Beautiful Explanation to ACF & PACF:
https://www.youtube.com/watch?v=-vSzKfqcTDg
My Session: 2nd Day - Complete Tutorial:
http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA-model-in-python/
https://github.com/seanabu/seanabu.github.io/blob/master/Seasonal_ARIMA_model_Portland_transit.ipynb
Second good option for Second day:
https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
https://github.com/aarshayj/Analytics_Vidhya/tree/master/Articles/Time_Series_Analysis
May be good for second day:
https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
Good Reference for SARIMAX Grid-Search:
https://www.datasciencecentral.com/profiles/blogs/tutorial-forecasting-with-seasonal-arima
Select PARAMETERS for Holt-Winters:
https://www.quora.com/How-do-I-calculate-the-ideal-values-for-Alpha-Beta-and-Gamma-parameters-in-Holt-Winters-method
HOLTS-WINTER CLEAR EXPLANATION:
https://medium.com/datadriveninvestor/how-to-build-exponential-smoothing-models-using-python-simple-exponential-smoothing-holt-and-da371189e1a1
Using SARIMA with FOURIER TRANSFORMATION:
https://medium.com/intive-developers/forecasting-time-series-with-multiple-seasonalities-using-tbats-in-python-398a00ac0e8a
Important Note on -- When to use what ??
https://www.analyticsvidhya.com/blog/2018/09/non-stationary-time-series-python/
BEST ARTICLE SO FAR: ----- to be used for CenturyLink
https://www.kaggle.com/kashnitsky/topic-9-part-1-time-series-analysis-in-python
Needs to be validated for CTL:
https://www.kaggle.com/berhag/co2-emission-forecast-with-python-seasonal-arima
PYRAMID MODEL --- AUTO-ARIMA:
https://www.analyticsvidhya.com/blog/2018/08/auto-arima-time-series-modeling-python-r/
To-DO ---- Residual Analysis:
https://machinelearningmastery.com/visualize-time-series-residual-forecast-errors-with-python/
Modelling Residual Data:
https://machinelearningmastery.com/model-residual-errors-correct-time-series-forecasts-python/
Auto-Arima:
https://medium.com/@josemarcialportilla/using-python-and-auto-arima-to-forecast-seasonal-time-series-90877adff03c
Excellent Statement:
https://www.kaggle.com/berhag/co2-emission-forecast-with-python-seasonal-arima
LSTM: Basics
https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/
LSTM: Advanced:
https://machinelearningmastery.com/lstm-model-architecture-for-rare-event-time-series-forecasting/
VAR:
https://www.analyticsvidhya.com/blog/2018/09/multivariate-time-series-guide-forecasting-modeling-python-codes/
https://dzone.com/articles/vector-autoregression-overview-and-proposals
Excellent tutorial on Holt-Winters Method:
https://grisha.org/blog/2016/01/29/triple-exponential-smoothing-forecasting/
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SLACK URL:
https://hackathon-q883311.slack.com/messages/DFNN8N16Y/
SLACK API URL:
https://api.slack.com/apps/AFP378MV3/slash-commands?saved=1
JIRA URL:
https://trail2019.atlassian.net/secure/Dashboard.jspa
JIRIO Guide:
https://jirioslackapp.com/usage/
Next-UP URL:
https://account.nextup.ai/account/create/jira?new=true&bot=5c4d7bf2b0401f78059acfbb
================
NYLAS GOOGLE CALENDAR BLOCKING -- GUIDE
https://www.nylas.com/blog/build-a-slack-bot-scheduler-in-30-minutes
https://b61c8ac9.ngrok.io
https://docs.nylas.com/reference#post-event
NYlas:
Secret Token:
9sRSOnGZNhv5dFbcvPdu7SS1FRnuOA
Nylas ID:
e6n08kbcad6qcu4imotu336m5
Google Calendar ID:
xb63qftus3f8loe51mrpxy35
=======================
FAST2SMS Token:
CU6njl7ZoP5D0QV1y3sIfxamMFRgOWG4cr9Bk8htvbuHSLzATXEC56b7MGUnfVpSWgLOXKrQdR3HPTNh
====================
CRONOFY SLACK CONNECTOR:
Access Token:
w3YKyPgk7doKcgoYAU-KecjNZFtrp6cw
Exchange Calendar
cal_XEqf@J9txQLeip7-_Xftg@rjtUhunTi-TmuyC4w
Google Calendar:
cal_XEqf@J9txQLeip7-_g2uVe-coFVE-qDiAr0hrng
============================
Slack bot for JIRA Integration:
https://chatbotslife.com/create-jira-tickets-in-slack-with-dialogs-7bcc40038ea2
https://chatbotslife.com/new-feature-create-jira-issues-from-slack-116e78e232bb
Next-UP URL:
https://account.nextup.ai
Application Name: nextupai
Consumer Key:
nextupai
Consumer Name:
nextupai
Public Key:
MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCLP6LnQNTH7iGSzT+TUWsdvy5k
D2ZEPwuY1BoyqP7WThmLl6Xli7P7r8OofC1ti0gnCfWLVzaeu8Yx+sl+9GdJJDWu
e1LCsoSatBPi4sgyymLZNB14Wyv7tTYumcN6N8tTvnKu9I6vUvn0PcYIXgbR9tjZ
4mgrJHdcC9FKSTbJGwIDAQAB
===================
Pre-built Intents & Agents for JIRA Integration:
https://github.com/intuit/benten/wiki/Create-BenTen-Agent-in-Dialog-flow
Sample Python Integration using CRONOFY:
https://app.cronofy.com/oauth/applications/5c4d55ccd0763e7a56f321ca/smart_invites/tutorial
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--- Infinera Links -----
STREAMSETS GUIDANCE:
https://hub.docker.com/r/streamsets/datacollector/
docker run --restart on-failure -p 18630:18630 -d --name streamsets-dc streamsets/datacollector dc
https://blogs.perficient.com/2016/08/22/how-to-load-logs-data-into-hdfs-using-flume/