CS460 Project :Weather forecast by time series forecast method.

BY: Haraprasad Dhal(sps 18) and Ravi Prakash Singh(sps 18)

Goals

• Our goal is to predict different quantities like average monthly temperature,rainfall of different location
• We will be using time series forecast method.
• We will try to do some changes or/and add something to these time series forecast method to improve the forecast.

Time Series

• A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence of observation taken at successive
  equally spaced points in time. e.g Daily Max and Min Temperature record
• 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.

Models:

• Auto regressive (AR) Model
• Integrated (I) Model
• Moving average(MR) Model
• Combination of these: autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models
• ARIMAX
• SARIMA
• SARIMAX

ARIMA Model:

• AR MODEL AR(p): we forecast the variable of interest using a linear combination of predictors. In an autoregression model, we forecast the
  variable of interest using a linear combination of past values of the variable.
  


• MA model MA(q):-Rather than using past values of the forecast variable in a regressio n, a moving average model uses past forecast errors.
  
  
  

ARIMA :

An ARIMA model is characterized by 3 terms: p, d, q It is basically a combination of previous mentioned model where,
p is the order of the AR term.
q is the order of the MA term.
d is the number of differencing required to make the time series stationary.
If a time series, has seasonal patterns, then you need to add seasonal terms and it becomes SARIMA, short for ‘Seasonal ARIMA’.

Challenge:

• SARIMAX and ARIMAX uses Exogenous variable(X) on which our prediction may depend. We are thinking to add some more Exogenous variables to our model.
• In case of weather forecast just a time evolution of a place cannot give us a good result.We are thinking of adding information about surrounding places
  to it as well. The major problem will be showing the connection between the places. We would like to make a model for a place so that it is coupled to surrounding places.

Plan:

UPTO MIDSEM

• We want study these Model in detail and use it (with python libraries) to predict maximum and minimum temperatures of selected local areas where we have the data.
• We would like to make experiments with the Exogenous variables (X) of SARIMAX and ARIMAX model to improve the forecast.

POST MIDSEM

• we will be thinking about some ways to tackle the above mentioned challenge and try to do the forecast in short time scale.

Work Division:

• Data Collection - Hara
• Data Refining-Ravi
• Feature engineering -Both
• implementation in python-Ravi
• HTML Site maintenance -Hara
• Thinking about the challenge -Both

Relevant paper:

• ARIMA based daily weather forecasting tool: A case study for Varanasi, by - NIKITA SHIVHARE, ATUL KUMAR RAHUL, SHYAM BIHARI DWIVEDI and PRABHAT KUMAR SINGH DIKSHIT. link
• Time series analysis of climate variables using seasonal ARIMA approach, by -TRIPTI DIMRI , SHAMSHAD AHMAD and MOHAMMAD SHARIF link
• Forecasting daily meteorological time series using ARIMA and regression models, by - Małgorzata Murat, Iwona Malinowska, Magdalena Gos, and Jaromir Krzyszczak link