adding readme

Author: Ajit Kumar Singh

.DS_Store

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 # Time-Series-Analysis-and-Forecasting-with-Python
 <p>Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.</p>
 <p>Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series. Let’s get started!</p>
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+## Contents
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+- **Time Series Data Visualization**
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+    - Plotting of Pandas Df
+    - Adding title
+    - Adding Axis label
+    - X limits by slice
+    - X limit by argument
+    - Color and Style
+    - X ticks spacing
+    - Date formatting
+    - Major and Minor axis values
+    - Gridlines
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+- **Time Series EDA**
+    
+    - Introduction with time series data
+    - Time resampling
+    - Time downsampling/upsampling
+    - Time Shifting
+    - forward shift
+    - backward shift
+    - Rolling window mean
+    - Expanding window mean/cummulative mean
+
+- **Time Series Data Analysis**
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+    - Introduction to statsmodels
+    - Hodrick Prescott filter - Trend/cyclical components
+    - Time Series Stationarity
+    - Augmented Dickey Fuller Test
+    - Granger Causality Tests
+    - Time series decomposition
+    - Additive/multiplicative models
+    - Moving Average
+    - Simple Exponentially weighted moving average(EWMA)
+    - Double EWMA
+    - Holt-Winters Method(Triple EWMA)
+
+- **Time Series Forecasting Classical Methods**
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+    - Forecasting with Holts-Winter Method
+    - Autocorrelation function(ACF)
+    - Partial autocorrelation function(PACF)
+    - Autocovariance for 1D
+    - Autocorrelation for 1D
+    - Autoregressive model(AR(p))
+    - Autoregressive Moving Average(ARMA) Model
+    - Autoregressive Integreted Moving Average(ARIMA)
+    - Error/Trend/Seasonal Decomposition(ETS Decomposition)
+    - Seasonal Autoregressive Integreted Moving Averages(SARIMA)
+    - Seasonal AutoRegressive Integreted Moving Average with EXogenous Variable.
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+- **Time Series Forecasting with Deep Learning**
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+    - LSTMs for time series forecasting
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