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IMPLEMENTATION Figure 5.1 Holt Linear Method Figure 5.2 Holt Winter Method 1

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Page 1: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

IMPLEMENTATION

Figure 5.1 Holt Linear Method

Figure 5.2 Holt Winter Method

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Page 2: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.3

Figure 5.4 Time Series of Land Average Temperature

Figure 5.5 Time Series Ocean Average temperature

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Page 3: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.7 Applying Log Transformation

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Page 4: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.8 Average Removal

Figure 5.9 Exponentially Weighted Average

Figure 5.10 Decomposition

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Page 5: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.11 Trend

Figure 5.12 Seasonality

Figure 5.13 Residual

Figure 5.14 Differencing

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Page 6: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.15 Checking for stationarity

Moving Average Model

Figure 5.16 Autocorrelation Function

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Page 7: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.17 Visualizations of results of MA model vs Predicted

Figure 5.18 Training series combined with forecasted series.

Autoregressive Model

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Page 8: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.19 Partial Autocorrelation Function

Figure 5.20 Visualization of AR model – Original vs Predicted

Figure 5.21 Training series combined with forecasted series for AR model

Figure 5.22 Training series combined with forecasted series for ARIMA mode

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Page 9: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Figure 5.23 LSTM model Forecasting vs Original

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Page 10: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

1. TESTING REPORTS

Iteration NO. - 1Forecasted = 0.795276 Expected = 0.685637 Error = 15.990916 %Iteration NO. - 2Forecasted = 0.680479 Expected = 0.638481 Error = 6.577787 %Iteration NO. - 3Forecasted = 0.636038 Expected = 0.628786 Error = 1.153256 %Iteration NO. - 4Forecasted = 0.627831 Expected = 1.257384 Error = 50.068498 %Iteration NO. - 5Forecasted = 1.295931 Expected = 1.931208 Error = 32.895295 %Iteration NO. - 6Forecasted = 1.965912 Expected = 1.345931 Error = 46.063348 %Iteration NO. - 7Forecasted = 1.322335 Expected = 1.296826 Error = 1.966984 %Iteration NO. - 8Forecasted = 1.294874 Expected = 1.173469 Error = 10.345742 %Iteration NO. - 9Forecasted = 1.167471 Expected = 1.072747 Error = 8.830041 %Iteration NO. - 10Iteration NO. - 90Forecasted = 1.456534 Expected = 1.381751 Error = 5.412183 %Iteration NO. - 91Forecasted = 1.377239 Expected = 1.322023 Error = 4.176629 %Iteration NO. - 92Forecasted = 1.318472 Expected = 1.178119 Error = 11.913305 %Iteration NO. - 93Forecasted = 1.170582 Expected = 1.037642 Error = 12.811719 %Iteration NO. - 94Forecasted = 1.030508 Expected = 0.980334 Error = 5.118133 %Iteration NO. - 95Forecasted = 0.977098 Expected = 0.880989 Error = 10.909163 %Iteration NO. - 96Forecasted = 0.875620 Expected = 0.830910 Error = 5.380812 %Iteration NO. - 97Forecasted = 0.827991 Expected = 0.688177 Error = 20.316611 %Iteration NO. - 98Forecasted = 0.681037 Expected = 0.742365 Error = 8.261229 %

RESULTS OF MA MODEL

Means in Temperature 14.900387 %RMSE in Temperature 3.860102 %

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Page 11: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

AUTO REGRESSIVE MODEL

Iteration NO. - 1Forecasted = 0.795032 Expected = 0.685637 Error = 15.955257 %Iteration NO. - 2Forecasted = 0.680225 Expected = 0.638481 Error = 6.538060 %Iteration NO. - 3Forecasted = 0.635792 Expected = 0.628786 Error = 1.114136 %Iteration NO. - 4Forecasted = 0.627739 Expected = 1.257384 Error = 50.075803 %Iteration NO. - 5Forecasted = 1.297061 Expected = 1.931208 Error = 32.836829 %Iteration NO. - 6Forecasted = 1.969929 Expected = 1.345931 Error = 46.361776 %Iteration NO. - 7Forecasted = 1.322836 Expected = 1.296826 Error = 2.005647 %Iteration NO. - 8Forecasted = 1.293816 Expected = 1.173469 Error = 10.255629 %Iteration NO. - 9Forecasted = 1.167282 Expected = 1.072747 Error = 8.812464 %Iteration NO. - 10Forecasted = 1.067544 Expected = 0.947476 Error = 12.672390 %Iteration NO. - 90Forecasted = 1.458513 Expected = 1.381751 Error = 5.555450 %Iteration NO. - 91Forecasted = 1.377730 Expected = 1.322023 Error = 4.213793 %Iteration NO. - 92Forecasted = 1.318193 Expected = 1.178119 Error = 11.889669 %Iteration NO. - 93Forecasted = 1.170235 Expected = 1.037642 Error = 12.778289 %Iteration NO. - 94Forecasted = 1.029988 Expected = 0.980334 Error = 5.065067 %Iteration NO. - 95Forecasted = 0.976696 Expected = 0.880989 Error = 10.863515 %Iteration NO. - 96Forecasted = 0.875350 Expected = 0.830910 Error = 5.348290 %Iteration NO. - 97Forecasted = 0.827684 Expected = 0.688177 Error = 20.272016 %Iteration NO. - 98Forecasted = 0.680728 Expected = 0.742365 Error = 8.302777 %

RESULTS OF AR MODEL

Means Error in Forecasted Temperature 14.889718 %RMSE in Forecasted Temperature 3.858720 %

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Page 12: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

ARIMA MODEL

Iteration NO. - 1Forecasted = 8.989412 Expected = 7.084000 Error = 6.724350 %Iteration NO. - 2Forecasted = 6.176126 Expected = 4.523000 Error = 9.137334 %Iteration NO. - 3Forecasted = 4.218238 Expected = 2.844000 Error = 12.080147 %Iteration NO. - 4Forecasted = 2.831279 Expected = 3.576000 Error = 5.206381 %Iteration NO. - 5Forecasted = 4.865507 Expected = 6.906000 Error = 7.386667 %Iteration NO. - 6Forecasted = 8.558189 Expected = 9.295000 Error = 1.981739 %Iteration NO. - 7Forecasted = 9.147603 Expected = 12.054000 Error = 6.027869 %Iteration NO. - 8Forecasted = 12.466564 Expected = 14.145000 Error = 2.966484 %Iteration NO. - 9Forecasted = 12.881055 Expected = 15.174000 Error = 3.777752 %Iteration NO. - 10Forecasted = 13.887150 Expected = 14.377000 Error = 0.851794 %Iteration NO. - 90Forecasted = 7.558453 Expected = 9.313000 Error = 4.709940 %Iteration NO. - 91Forecasted = 9.893559 Expected = 12.312000 Error = 4.910739 %Iteration NO. - 92Forecasted = 12.287710 Expected = 14.505000 Error = 3.821596 %Iteration NO. - 93Forecasted = 13.482159 Expected = 15.051000 Error = 2.605875 %Iteration NO. - 94Forecasted = 13.416783 Expected = 14.755000 Error = 2.267396 %Iteration NO. - 95Forecasted = 13.102832 Expected = 12.999000 Error = 0.199693 %Iteration NO. - 96Forecasted = 11.205073 Expected = 10.801000 Error = 0.935268 %Iteration NO. - 97Forecasted = 9.578170 Expected = 7.433000 Error = 7.215021 %Iteration NO. - 98Forecasted = 6.354373 Expected = 5.518000 Error = 3.789294 %

RESULTS OF ARIMA MODEL

Means Error in Forecasted Temperature 4.222607 %RMSE in Forecasted Temperature 2.054898 %

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Page 13: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

LSTM

Forecasted = -0.388320 Expected = 0.055792 Error = 5.960Forecasted = 0.139059 Expected = -0.039647 Error = 2.507Forecasted = 0.506904 Expected = -0.165015 Error = 2.072Forecasted = 0.474063 Expected = -0.304027 Error = 0.559Forecasted = 0.263254 Expected = -0.518609 Error = 0.492Forecasted = 0.140694 Expected = -0.582915 Error = 0.759Forecasted = 0.039507 Expected = -0.409169 Error = 0.903Forecasted = -0.023897 Expected = 0.282948 Error = 0.916Forecasted = -0.121186 Expected = 0.415358 Error = 0.708Forecasted = -0.223527 Expected = 0.446757 Error = 0.500Forecasted = -0.376543 Expected = 0.289116 Error = 0.302Forecasted = -0.556477 Expected = 0.141549 Error = 2.931Forecasted = -0.107204 Expected = 0.046883 Error = 1.287Forecasted = 0.136038 Expected = -0.048788 Error = 1.788Forecasted = 0.394031 Expected = -0.179228 Error = 1.198Forecasted = 0.367762 Expected = -0.325560 Error = 0.130Forecasted = 0.298924 Expected = -0.547308 Error = 0.454Forecasted = 0.177801 Expected = -0.561088 Error = 0.683Forecasted = 0.029423 Expected = -0.367726 Error = 0.920Forecasted = -0.017550 Expected = 0.246805 Error = 0.929Forecasted = -0.113975 Expected = 0.408526 Error = 0.721Forecasted = -0.248863 Expected = 0.436690 Error = 0.430Forecasted = -0.323145 Expected = 0.276639 Error = 0.168Forecasted = -0.452062 Expected = 0.131993 Error = 2.425Forecasted = -0.235712 Expected = 0.040278 Error = 4.852Forecasted = -0.064181 Expected = -0.054756 Error = 0.172Forecasted = 0.600092 Expected = -0.184701 Error = 2.249Forecasted = 0.407762 Expected = -0.334667 Error = 0.218Forecasted = 0.271651 Expected = -0.553932 Error = 0.510Forecasted = 0.130437 Expected = -0.543561 Error = 0.760Forecasted = 0.036843 Expected = -0.330946 Error = 0.889Forecasted = 0.000134 Expected = 0.220847 Error = 0.999Forecasted = -0.127757 Expected = 0.402400 Error = 0.683Forecasted = -0.236873 Expected = 0.427770 Error = 0.446Forecasted = -0.431006 Expected = 0.267579 Error = 0.611Forecasted = -0.325067 Expected = 0.128169 Error = 1.536

LSTM RESULT

Mean Error in Forecasted Temperature 1.404629 %RMSE in Forecasted Temperature 1.185170 %

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Page 14: ayushiasthana.files.wordpress.com · Web viewFigure 5.6 Checking the stationarity using Rolling mean and Standard Deviation

Table 7.1 Comparison of various Models

MODEL NAME RMSE

Holt’s Linear Trend Method 17.12790830144587

Holt’s Winter Model 4.427087095092175

AR 3.858720 %

MA 3.860102 %

ARIMA 2.054898 %

LSTM 1.185170 %

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